In this paper,we offer a review of type-3 fuzzy logic systems and their applications in control.The main objective of this work is to observe and analyze in detail the applications in the control area using type-3 fuz...In this paper,we offer a review of type-3 fuzzy logic systems and their applications in control.The main objective of this work is to observe and analyze in detail the applications in the control area using type-3 fuzzy logic systems.In this case,we review their most important applications in control and other related topics with type-3 fuzzy systems.Intelligent algorithms have been receiving increasing attention in control and for this reason a review in this area is important.This paper reviews the main applications that make use of Intelligent Computing methods.Specifically,type-3 fuzzy logic systems.The aim of this research is to be able to appreciate,in detail,the applications in control systems and to point out the scientific trends in the use of Intelligent Computing techniques.This is done with the construction and visualization of bibliometric networks,developed with VosViewer Software,which it is a free Java-based program,mainly intended to be used for analyzing and visualizing bibliometric networks.With this tool,we can create maps of publications,authors,or journals based on a co-citation network or construct maps of keywords,countries based on a co-occurrence networks,research groups,etc.展开更多
The traditional fuzzy logic system （FLS） can only model and control the process in two-dimensional nature. Many of real-world systems are of multidimensional features, such as, thermal and fluid processes with spati...The traditional fuzzy logic system （FLS） can only model and control the process in two-dimensional nature. Many of real-world systems are of multidimensional features, such as, thermal and fluid processes with spatiotemporal dynamics, biological systems, or decision-making processes that contain stochastic and imprecise uncertainties. These types of systems are difficult for the traditional FLS to model and control because they require a third dimension for spatial or probabilistic information. The type-2 fuzzy set provides the possibility to develop a three-dimensional fuzzy logic system for modeling and controlling these processes in three-dimensional nature.展开更多
A fuzzy model was presented to predict the weldment shape profile of submerged arc welds （SAW） including the shape of heat affected zone （HAZ）. The SAW bead-on-plates were welded by following a full factorial desi...A fuzzy model was presented to predict the weldment shape profile of submerged arc welds （SAW） including the shape of heat affected zone （HAZ）. The SAW bead-on-plates were welded by following a full factorial design matrix. The design matrix consisted of three levels of input welding process parameters. The welds were cross-sectioned and etched, and the zones were measured. A mapping technique was used to measure the various segments of the weld zones. These mapped zones were used to build a fuzzy logic model. The membership functions of the fuzzy model were chosen for the accurate prediction of the weld zone. The fuzzy model was further tested for a set of test case data. The weld zone predicted by the fuzzy logic model was compared with the experimentally obtained shape profiles and close agreement between the two was noted. The mapping technique developed for the weld zones and the fuzzy logiemodel earl be used for on-line control of the SAW process. From the SAW fuzzy logic model an estimation of the fusion and HAZ can also be developed.展开更多
This paper presents an observer based dynamic fuzzy logic system （DFLS） scheme for a class of unknown single-input single-output （SISO） nonlinear dynamic systems with external disturbances. The proposed approach d...This paper presents an observer based dynamic fuzzy logic system （DFLS） scheme for a class of unknown single-input single-output （SISO） nonlinear dynamic systems with external disturbances. The proposed approach does not need the availability of the state variables. Within this scheme, the DFLS is employed to identify the unknown nonlinear dynamic system. The control law and parameter adaptation laws of the DFLS are derived based on Lyapunov synthesis approach. The control law is robustfied in H∞ sense to attenuate external disturbance, model uncertainties, and fuzzy approximation errors. It is shown that under appropriate assumptions, it guarantees the boundedness of all the signals in the closed-loop system and the asymptotic convergence to zero of tracking errors. The proposed method is applied to an inverted pendulum system to verify the effectiveness of the proposed algorithms.展开更多
The estimation of the fuzzy membership function parameters for interval type 2 fuzzy logic system(IT2-FLS)is a challenging task in the presence of uncertainty and imprecision.Grasshopper optimization algorithm(GOA)is ...The estimation of the fuzzy membership function parameters for interval type 2 fuzzy logic system(IT2-FLS)is a challenging task in the presence of uncertainty and imprecision.Grasshopper optimization algorithm(GOA)is a fresh population based meta-heuristic algorithm that mimics the swarming behavior of grasshoppers in nature,which has good convergence ability towards optima.The main objective of this paper is to apply GOA to estimate the optimal parameters of the Gaussian membership function in an IT2-FLS.The antecedent part parameters(Gaussian membership function parameters)are encoded as a population of artificial swarm of grasshoppers and optimized using its algorithm.Tuning of the consequent part parameters are accomplished using extreme learning machine.The optimized IT2-FLS(GOAIT2FELM)obtained the optimal premise parameters based on tuned consequent part parameters and is then applied on the Australian national electricity market data for the forecasting of electricity loads and prices.The forecasting performance of the proposed model is compared with other population-based optimized IT2-FLS including genetic algorithm and artificial bee colony optimization algorithm.Analysis of the performance,on the same data-sets,reveals that the proposed GOAIT2FELM could be a better approach for improving the accuracy of the IT2-FLS as compared to other variants of the optimized IT2-FLS.展开更多
Cancelled the first axiom L1) or the third axiom L3) of the classical formal logic system we established two kinds of quasi-formal deductive system, LG-R^* and LG^* respectively. In LG-R^* we proved that neither the d...Cancelled the first axiom L1) or the third axiom L3) of the classical formal logic system we established two kinds of quasi-formal deductive system, LG-R^* and LG^* respectively. In LG-R^* we proved that neither the deduction theorem nor the hypothetical syllogism (HS) rule held but a deduction theorem and an HS rule are obtained in a weak sense. We also proved that both the deduction theorem and the hypothetical syllogism(HS) rule hold in LG^*.展开更多
The wireless sensor network(WSN)is a growing sector in the network domain.By implementing it many industries developed smart task for different purposes.Sensor nodes interact with each other and this interaction techn...The wireless sensor network(WSN)is a growing sector in the network domain.By implementing it many industries developed smart task for different purposes.Sensor nodes interact with each other and this interaction technique are handled by different routing protocol.Extending the life of the network in WSN is a challenging issue because energy in sensor nodes are quickly drained.So the overall performance of WSN are degraded by this limitation.To resolve this unreliable low power link,many researches have provided various routing protocols to make the network as dependable and sustainable as possible.While speeding up the data delivery is also considered to be an effective approach to save energy.To achieve this objective,we propose a new energy efficient routing protocol using genetic fuzzy logic system.Our primary objective is to save energy by sending data packets via the shortest path.Numerous studies have proved that the clustering protocol plays an important role in prolonging the life of the sensor node in theWSN.Keeping up with this our second objective is selection of head node from a cluster.This cluster head is selected based on the availability of maximum residual energy among the nodes,lifetime of head-to-head link,and its minimum distance to the base station.The genetic fitness approach is proposed for optimal routing and the selection of cluster head(CH)is employed with fuzzy logic system.As a result,the genetic fuzzy logic system(GFLS)can effectively accelerate the process to solve this problem.MATLAB is used to deploy nodes inWSN.The performance is calculated in terms of efficiency,delay,packet delivery rate and network throughput.The performance is compared with previous pertinent work.The proposed approach has elevated its performance around 8%in packet delivery and 6%in overall network throughput.展开更多
This research paper tackles the complexities of achieving global fuzzy consensus in leader-follower systems in robotic systems,focusing on robust control systems against an advanced signal attack that integrates senso...This research paper tackles the complexities of achieving global fuzzy consensus in leader-follower systems in robotic systems,focusing on robust control systems against an advanced signal attack that integrates sensor and actuator disturbances within the dynamics of follower robots.Each follower robot has unknown dynamics and control inputs,which expose it to the risks of both sensor and actuator attacks.The leader robot,described by a secondorder,time-varying nonlinear model,transmits its position,velocity,and acceleration information to follower robots through a wireless connection.To handle the complex setup and communication among robots in the network,we design a robust hybrid distributed adaptive control strategy combining the effect of sensor and actuator attack,which ensures asymptotic consensus,extending beyond conventional bounded consensus results.The proposed framework employs fuzzy logic systems(FLSs)as proactive controllers to estimate unknown nonlinear behaviors,while also effectively managing sensor and actuator attacks,ensuring stable consensus among all agents.To counter the impact of the combined signal attack on follower dynamics,a specialized robust control mechanism is designed,sustaining system stability and performance under adversarial conditions.The efficiency of this control strategy is demonstrated through simulations conducted across two different directed communication topologies,underscoring the protocol’s adaptability,resilience,and effectiveness in maintaining global consensus under complex attack scenarios.展开更多
Wind energy has emerged as a potential replacement for fossil fuel-based energy sources.To harness maximum wind energy,a crucial decision in the development of an efficient wind farm is the optimal layout design.This ...Wind energy has emerged as a potential replacement for fossil fuel-based energy sources.To harness maximum wind energy,a crucial decision in the development of an efficient wind farm is the optimal layout design.This layout defines the specific locations of the turbines within the wind farm.The process of finding the optimal locations of turbines,in the presence of various technical and technological constraints,makes the wind farm layout design problem a complex optimization problem.This problem has traditionally been solved with nature-inspired algorithms with promising results.The performance and convergence of nature-inspired algorithms depend on several parameters,among which the algorithm termination criterion plays a crucial role.Timely convergence is an important aspect of efficient algorithm design because an inefficient algorithm results in wasted computational resources,unwarranted electricity consumption,and hardware stress.This study provides an in-depth analysis of several termination criteria while using the genetic algorithm as a test bench,with its application to the wind farm layout design problem while considering various wind scenarios.The performance of six termination criteria is empirically evaluated with respect to the quality of solutions produced and the execution time involved.Due to the conflicting nature of these two attributes,fuzzy logic-based multi-attribute decision-making is employed in the decision process.Results for the fuzzy decision approach indicate that among the various criteria tested,the criterion Phi achieves an improvement in the range of 2.44%to 32.93%for wind scenario 1.For scenario 2,Best-worst termination criterion performed well compared to the other criteria evaluated,with an improvement in the range of 1.2%to 9.64%.For scenario 3,Hitting bound was the best performer with an improvement of 1.16%to 20.93%.展开更多
Model checking is an automated formal verification method to verify whether epistemic multi-agent systems adhere to property specifications.Although there is an extensive literature on qualitative properties such as s...Model checking is an automated formal verification method to verify whether epistemic multi-agent systems adhere to property specifications.Although there is an extensive literature on qualitative properties such as safety and liveness,there is still a lack of quantitative and uncertain property verifications for these systems.In uncertain environments,agents must make judicious decisions based on subjective epistemic.To verify epistemic and measurable properties in multi-agent systems,this paper extends fuzzy computation tree logic by introducing epistemic modalities and proposing a new Fuzzy Computation Tree Logic of Knowledge(FCTLK).We represent fuzzy multi-agent systems as distributed knowledge bases with fuzzy epistemic interpreted systems.In addition,we provide a transformation algorithm from fuzzy epistemic interpreted systems to fuzzy Kripke structures,as well as transformation rules from FCTLK formulas to Fuzzy Computation Tree Logic(FCTL)formulas.Accordingly,we transform the FCTLK model checking problem into the FCTL model checking.This enables the verification of FCTLK formulas by using the fuzzy model checking algorithm of FCTL without additional computational overheads.Finally,we present correctness proofs and complexity analyses of the proposed algorithms.Additionally,we further illustrate the practical application of our approach through an example of a train control system.展开更多
In this study, we are first examining well-known approach to improve fuzzy reasoning model (FRM) by use of the genetic-based learning mechanism [1]. Later we propose our alternative way to build FRM, which has signifi...In this study, we are first examining well-known approach to improve fuzzy reasoning model (FRM) by use of the genetic-based learning mechanism [1]. Later we propose our alternative way to build FRM, which has significant precision advantages and does not require any adjustment/learning. We put together neuro-fuzzy system (NFS) to connect the set of exemplar input feature vectors (FV) with associated output label (target), both represented by their membership functions (MF). Next unknown FV would be classified by getting upper value of current output MF. After that the fuzzy truths for all MF upper values are maximized and the label of the winner is considered as the class of the input FV. We use the knowledge in the exemplar-label pairs directly with no training. It sets up automatically and then classifies all input FV from the same population as the exemplar FVs. We show that our approach statistically is almost twice as accurate, as well-known genetic-based learning mechanism FRM.展开更多
Based on the Schweizer-Sklar t-norm, a fuzzy logic system UL^＊ is established, and its soundness theorem and completeness theorem are proved. The following facts are pointed out＂ the well-known formal system SBL~ is...Based on the Schweizer-Sklar t-norm, a fuzzy logic system UL^＊ is established, and its soundness theorem and completeness theorem are proved. The following facts are pointed out＂ the well-known formal system SBL~ is a semantic extension of UL^＊; the fuzzy logic system IMTL△ is a special case of UL^＊ when two negations in UL^＊ coincide. Moreover, the connections between the system UL^＊ and some fuzzy logic formal systems are investigated. Finally, starting from the concepts of ＂the strength of an‘AND＇ operator＂ by R.R. Yager and ＂the strength of fuzzy rule interaction＂ by T. Whalen, the essential meaning of a parameter p in UL^＊ is explained and the use of fuzzy logic system UL^＊ in approximate reasoning is presented.展开更多
The paper deals with the application of Volterra bound Interval type−2 fuzzy logic techniques in power quality assessment.This work proposes a new layout for detection,localization and classification of various types ...The paper deals with the application of Volterra bound Interval type−2 fuzzy logic techniques in power quality assessment.This work proposes a new layout for detection,localization and classification of various types of power quality events.The proposed method exploits Volterra series for the extraction of relevant features,which are used to recognize different PQ events by Interval type-2 fuzzy logic based classifier.Numerous single as well as multiple powers signal disturbances have been simulated to testify the efficiency of the proposed technique.This time–frequency analysis results in the clear visual detection,localization,and classification of the different power quality events.The simulation results signify that the proposed scheme has a higher recognition rate while classifying single and multiple power quality events unlike other methods.Finally,the proposed method is compared with SVM,feed forward neural network and type−1 Fuzzy logic system based classifier to show the efficacy of the proposed technique in classifying the Power quality events.展开更多
For a class of chaotic systems with unknown functions and disturbances,asymptotic stabilisation of the chaotic systems is achieved by designing a stability controller based on the adaptive fuzzy logic systems.At first...For a class of chaotic systems with unknown functions and disturbances,asymptotic stabilisation of the chaotic systems is achieved by designing a stability controller based on the adaptive fuzzy logic systems.At first,based on the universal approximation property of fuzzy logic systems,the Mamdani-type fuzzy logic systems with the parameter adaptive laws are designed utilising the data information sampled from the inputs and outputs of unknown functions in the chaotic systems,then the fuzzy logic systems are used to design the stability controller with three parameter adaptive laws,but the three parameters have no relationship with the number of fuzzy rules,so the stability controller is not only able to achieve asymptotic stabilisation for the chaotic system’s states,but also to reduce the number of fuzzy rules and the no-line computational burden significantly.Finally,simulations are used to show the validity of the stabilisation method.展开更多
This study aims to predict the undrained shear strength of remolded soil samples using non-linear regression analyses,fuzzy logic,and artificial neural network modeling.A total of 1306 undrained shear strength results...This study aims to predict the undrained shear strength of remolded soil samples using non-linear regression analyses,fuzzy logic,and artificial neural network modeling.A total of 1306 undrained shear strength results from 230 different remolded soil test settings reported in 21 publications were collected,utilizing six different measurement devices.Although water content,plastic limit,and liquid limit were used as input parameters for fuzzy logic and artificial neural network modeling,liquidity index or water content ratio was considered as an input parameter for non-linear regression analyses.In non-linear regression analyses,12 different regression equations were derived for the prediction of undrained shear strength of remolded soil.Feed-Forward backpropagation and the TANSIG transfer function were used for artificial neural network modeling,while the Mamdani inference system was preferred with trapezoidal and triangular membership functions for fuzzy logic modeling.The experimental results of 914 tests were used for training of the artificial neural network models,196 for validation and 196 for testing.It was observed that the accuracy of the artificial neural network and fuzzy logic modeling was higher than that of the non-linear regression analyses.Furthermore,a simple and reliable regression equation was proposed for assessments of undrained shear strength values with higher coefficients of determination.展开更多
Increasing Internet of Things(IoT)device connectivity makes botnet attacks more dangerous,carrying catastrophic hazards.As IoT botnets evolve,their dynamic and multifaceted nature hampers conventional detection method...Increasing Internet of Things(IoT)device connectivity makes botnet attacks more dangerous,carrying catastrophic hazards.As IoT botnets evolve,their dynamic and multifaceted nature hampers conventional detection methods.This paper proposes a risk assessment framework based on fuzzy logic and Particle Swarm Optimization(PSO)to address the risks associated with IoT botnets.Fuzzy logic addresses IoT threat uncertainties and ambiguities methodically.Fuzzy component settings are optimized using PSO to improve accuracy.The methodology allows for more complex thinking by transitioning from binary to continuous assessment.Instead of expert inputs,PSO data-driven tunes rules and membership functions.This study presents a complete IoT botnet risk assessment system.The methodology helps security teams allocate resources by categorizing threats as high,medium,or low severity.This study shows how CICIoT2023 can assess cyber risks.Our research has implications beyond detection,as it provides a proactive approach to risk management and promotes the development of more secure IoT environments.展开更多
Short-duration heavy rainfall(SHR),as delineated by the National Meteorological Center of the China Me-teorological Administration,is characterized by hourly rainfall amounts no less than 20.0 mm.SHR is one of the mos...Short-duration heavy rainfall(SHR),as delineated by the National Meteorological Center of the China Me-teorological Administration,is characterized by hourly rainfall amounts no less than 20.0 mm.SHR is one of the most common convective weather phenomena that can cause severe damage.Short-range forecasting of SHR is an important part of operational severe weather prediction.In the present study,an improved objective SHR forecasting scheme was developed by adopting the ingredients-based methodology and using the fuzzy logic approach.The 1.0°×1.0°National Centers for Environmental Prediction(NCEP)final analysis data and the ordinary rainfall(0.1-19.9 mm h-1)and SHR observational data from 411 stations were used in the improved scheme.The best lifted index,the total precipitable water,the 925 hPa specific humidity(Q 925),and the 925 hPa divergence(DIV 925)were selected as predictors based on objective analysis.Continuously distributed membership functions of predictors were obtained based on relative frequency analysis.The weights of predictors were also objectively determined.Experiments with a typhoon SHR case and a spring SHR case show that the main possible areas could be captured by the improved scheme.Verification of SHR forecasts within 96 hours with NCEP global forecasts 1.0°×1.0°data initiated at 08:00 Beijing Time during the warm seasons in 2015 show the results were improved from both deterministic and probabilistic perspectives.This study provides an objectively feasible choice for short-range guidance forecasts of SHR.The scheme can be applied to other convective phenomena.展开更多
The position control system of an electro-hydraulic actuator system （EHAS） is investigated in this paper. The EHAS is developed by taking into consideration the nonlinearities of the system： the friction and the in...The position control system of an electro-hydraulic actuator system （EHAS） is investigated in this paper. The EHAS is developed by taking into consideration the nonlinearities of the system： the friction and the internal leakage. A variable load that simulates a realistic load in robotic excavator is taken as the trajectory reference. A method of control strategy that is implemented by employing a fuzzy logic controller （FLC） whose parameters are optimized using particle swarm optimization （PSO） is proposed. The scaling factors of the fuzzy inference system are tuned to obtain the optimal values which yield the best system performance. The simulation results show that the FLC is able to track the trajectory reference accurately for a range of values of orifice opening. Beyond that range, the orifice opening may introduce chattering, which the FLC alone is not sufficient to overcome. The PSO optimized FLC can reduce the chattering significantly. This result justifies the implementation of the proposed method in position control of EHAS.展开更多
To deal with fault detection and diagnosis with incomplete model for dead reckoning system of mobile robot,an integrative framework of particle filter detection and fuzzy logic diagnosis was devised.Firstly,an adaptiv...To deal with fault detection and diagnosis with incomplete model for dead reckoning system of mobile robot,an integrative framework of particle filter detection and fuzzy logic diagnosis was devised.Firstly,an adaptive fault space is designed for recognizing both known faults and unknown faults,in corresponding modes of modeled and model-free.Secondly,the particle filter is utilized to diagnose the modeled faults and detect model-free fault according to the low particle weight and reliability.Especially,the proposed fuzzy logic diagnosis can further analyze model-free modes and identify some soft faults in unknown fault space.The MORCS-1 experimental results show that the fuzzy diagnosis particle filter(FDPF) combinational framework improves fault detection and identification completeness.Specifically speaking,FDPF is feasible to diagnose the modeled faults in known space.Furthermore,the types of model-free soft faults can also be further identified and diagnosed in unknown fault space.展开更多
One of the most dangerous safety hazard in underground coal mines is roof falls during retreat mining.Roof falls may cause life-threatening and non-fatal injuries to miners and impede mining and transportation operati...One of the most dangerous safety hazard in underground coal mines is roof falls during retreat mining.Roof falls may cause life-threatening and non-fatal injuries to miners and impede mining and transportation operations.As a result,a reliable roof fall prediction model is essential to tackle such challenges.Different parameters that substantially impact roof falls are ill-defined and intangible,making this an uncertain and challenging research issue.The National Institute for Occupational Safety and Health assembled a national database of roof performance from 37 coal mines to explore the factors contributing to roof falls.Data acquired for 37 mines is limited due to several restrictions,which increased the likelihood of incompleteness.Fuzzy logic is a technique for coping with ambiguity,incompleteness,and uncertainty.Therefore,In this paper,the fuzzy inference method is presented,which employs a genetic algorithm to create fuzzy rules based on 109 records of roof fall data and pattern search to refine the membership functions of parameters.The performance of the deployed model is evaluated using statistical measures such as the Root-Mean-Square Error,Mean-Absolute-Error,and coefficient of determination(R_(2)).Based on these criteria,the suggested model outperforms the existing models to precisely predict roof fall rates using fewer fuzzy rules.展开更多
基金CONAHCYTTecnológico Nacional de Mexico/Tijuana Institute of Technology for the support during this research
文摘In this paper,we offer a review of type-3 fuzzy logic systems and their applications in control.The main objective of this work is to observe and analyze in detail the applications in the control area using type-3 fuzzy logic systems.In this case,we review their most important applications in control and other related topics with type-3 fuzzy systems.Intelligent algorithms have been receiving increasing attention in control and for this reason a review in this area is important.This paper reviews the main applications that make use of Intelligent Computing methods.Specifically,type-3 fuzzy logic systems.The aim of this research is to be able to appreciate,in detail,the applications in control systems and to point out the scientific trends in the use of Intelligent Computing techniques.This is done with the construction and visualization of bibliometric networks,developed with VosViewer Software,which it is a free Java-based program,mainly intended to be used for analyzing and visualizing bibliometric networks.With this tool,we can create maps of publications,authors,or journals based on a co-citation network or construct maps of keywords,countries based on a co-occurrence networks,research groups,etc.
基金supported by the National 973 Fundamental Research Program of China (No.2005CB724102,2006CB705404)
文摘The traditional fuzzy logic system （FLS） can only model and control the process in two-dimensional nature. Many of real-world systems are of multidimensional features, such as, thermal and fluid processes with spatiotemporal dynamics, biological systems, or decision-making processes that contain stochastic and imprecise uncertainties. These types of systems are difficult for the traditional FLS to model and control because they require a third dimension for spatial or probabilistic information. The type-2 fuzzy set provides the possibility to develop a three-dimensional fuzzy logic system for modeling and controlling these processes in three-dimensional nature.
基金Supported by the IIT Roorkee Project under Grant No. FIG-A Scheme-A
文摘A fuzzy model was presented to predict the weldment shape profile of submerged arc welds （SAW） including the shape of heat affected zone （HAZ）. The SAW bead-on-plates were welded by following a full factorial design matrix. The design matrix consisted of three levels of input welding process parameters. The welds were cross-sectioned and etched, and the zones were measured. A mapping technique was used to measure the various segments of the weld zones. These mapped zones were used to build a fuzzy logic model. The membership functions of the fuzzy model were chosen for the accurate prediction of the weld zone. The fuzzy model was further tested for a set of test case data. The weld zone predicted by the fuzzy logic model was compared with the experimentally obtained shape profiles and close agreement between the two was noted. The mapping technique developed for the weld zones and the fuzzy logiemodel earl be used for on-line control of the SAW process. From the SAW fuzzy logic model an estimation of the fusion and HAZ can also be developed.
文摘This paper presents an observer based dynamic fuzzy logic system （DFLS） scheme for a class of unknown single-input single-output （SISO） nonlinear dynamic systems with external disturbances. The proposed approach does not need the availability of the state variables. Within this scheme, the DFLS is employed to identify the unknown nonlinear dynamic system. The control law and parameter adaptation laws of the DFLS are derived based on Lyapunov synthesis approach. The control law is robustfied in H∞ sense to attenuate external disturbance, model uncertainties, and fuzzy approximation errors. It is shown that under appropriate assumptions, it guarantees the boundedness of all the signals in the closed-loop system and the asymptotic convergence to zero of tracking errors. The proposed method is applied to an inverted pendulum system to verify the effectiveness of the proposed algorithms.
文摘The estimation of the fuzzy membership function parameters for interval type 2 fuzzy logic system(IT2-FLS)is a challenging task in the presence of uncertainty and imprecision.Grasshopper optimization algorithm(GOA)is a fresh population based meta-heuristic algorithm that mimics the swarming behavior of grasshoppers in nature,which has good convergence ability towards optima.The main objective of this paper is to apply GOA to estimate the optimal parameters of the Gaussian membership function in an IT2-FLS.The antecedent part parameters(Gaussian membership function parameters)are encoded as a population of artificial swarm of grasshoppers and optimized using its algorithm.Tuning of the consequent part parameters are accomplished using extreme learning machine.The optimized IT2-FLS(GOAIT2FELM)obtained the optimal premise parameters based on tuned consequent part parameters and is then applied on the Australian national electricity market data for the forecasting of electricity loads and prices.The forecasting performance of the proposed model is compared with other population-based optimized IT2-FLS including genetic algorithm and artificial bee colony optimization algorithm.Analysis of the performance,on the same data-sets,reveals that the proposed GOAIT2FELM could be a better approach for improving the accuracy of the IT2-FLS as compared to other variants of the optimized IT2-FLS.
文摘Cancelled the first axiom L1) or the third axiom L3) of the classical formal logic system we established two kinds of quasi-formal deductive system, LG-R^* and LG^* respectively. In LG-R^* we proved that neither the deduction theorem nor the hypothetical syllogism (HS) rule held but a deduction theorem and an HS rule are obtained in a weak sense. We also proved that both the deduction theorem and the hypothetical syllogism(HS) rule hold in LG^*.
文摘The wireless sensor network(WSN)is a growing sector in the network domain.By implementing it many industries developed smart task for different purposes.Sensor nodes interact with each other and this interaction technique are handled by different routing protocol.Extending the life of the network in WSN is a challenging issue because energy in sensor nodes are quickly drained.So the overall performance of WSN are degraded by this limitation.To resolve this unreliable low power link,many researches have provided various routing protocols to make the network as dependable and sustainable as possible.While speeding up the data delivery is also considered to be an effective approach to save energy.To achieve this objective,we propose a new energy efficient routing protocol using genetic fuzzy logic system.Our primary objective is to save energy by sending data packets via the shortest path.Numerous studies have proved that the clustering protocol plays an important role in prolonging the life of the sensor node in theWSN.Keeping up with this our second objective is selection of head node from a cluster.This cluster head is selected based on the availability of maximum residual energy among the nodes,lifetime of head-to-head link,and its minimum distance to the base station.The genetic fitness approach is proposed for optimal routing and the selection of cluster head(CH)is employed with fuzzy logic system.As a result,the genetic fuzzy logic system(GFLS)can effectively accelerate the process to solve this problem.MATLAB is used to deploy nodes inWSN.The performance is calculated in terms of efficiency,delay,packet delivery rate and network throughput.The performance is compared with previous pertinent work.The proposed approach has elevated its performance around 8%in packet delivery and 6%in overall network throughput.
文摘This research paper tackles the complexities of achieving global fuzzy consensus in leader-follower systems in robotic systems,focusing on robust control systems against an advanced signal attack that integrates sensor and actuator disturbances within the dynamics of follower robots.Each follower robot has unknown dynamics and control inputs,which expose it to the risks of both sensor and actuator attacks.The leader robot,described by a secondorder,time-varying nonlinear model,transmits its position,velocity,and acceleration information to follower robots through a wireless connection.To handle the complex setup and communication among robots in the network,we design a robust hybrid distributed adaptive control strategy combining the effect of sensor and actuator attack,which ensures asymptotic consensus,extending beyond conventional bounded consensus results.The proposed framework employs fuzzy logic systems(FLSs)as proactive controllers to estimate unknown nonlinear behaviors,while also effectively managing sensor and actuator attacks,ensuring stable consensus among all agents.To counter the impact of the combined signal attack on follower dynamics,a specialized robust control mechanism is designed,sustaining system stability and performance under adversarial conditions.The efficiency of this control strategy is demonstrated through simulations conducted across two different directed communication topologies,underscoring the protocol’s adaptability,resilience,and effectiveness in maintaining global consensus under complex attack scenarios.
基金funded by King Fahd University of Petroleum&Minerals,Saudi Arabia under IRC-SES grant#INRE 2217.
文摘Wind energy has emerged as a potential replacement for fossil fuel-based energy sources.To harness maximum wind energy,a crucial decision in the development of an efficient wind farm is the optimal layout design.This layout defines the specific locations of the turbines within the wind farm.The process of finding the optimal locations of turbines,in the presence of various technical and technological constraints,makes the wind farm layout design problem a complex optimization problem.This problem has traditionally been solved with nature-inspired algorithms with promising results.The performance and convergence of nature-inspired algorithms depend on several parameters,among which the algorithm termination criterion plays a crucial role.Timely convergence is an important aspect of efficient algorithm design because an inefficient algorithm results in wasted computational resources,unwarranted electricity consumption,and hardware stress.This study provides an in-depth analysis of several termination criteria while using the genetic algorithm as a test bench,with its application to the wind farm layout design problem while considering various wind scenarios.The performance of six termination criteria is empirically evaluated with respect to the quality of solutions produced and the execution time involved.Due to the conflicting nature of these two attributes,fuzzy logic-based multi-attribute decision-making is employed in the decision process.Results for the fuzzy decision approach indicate that among the various criteria tested,the criterion Phi achieves an improvement in the range of 2.44%to 32.93%for wind scenario 1.For scenario 2,Best-worst termination criterion performed well compared to the other criteria evaluated,with an improvement in the range of 1.2%to 9.64%.For scenario 3,Hitting bound was the best performer with an improvement of 1.16%to 20.93%.
基金The work is partially supported by Natural Science Foundation of Ningxia(Grant No.AAC03300)National Natural Science Foundation of China(Grant No.61962001)Graduate Innovation Project of North Minzu University(Grant No.YCX23152).
文摘Model checking is an automated formal verification method to verify whether epistemic multi-agent systems adhere to property specifications.Although there is an extensive literature on qualitative properties such as safety and liveness,there is still a lack of quantitative and uncertain property verifications for these systems.In uncertain environments,agents must make judicious decisions based on subjective epistemic.To verify epistemic and measurable properties in multi-agent systems,this paper extends fuzzy computation tree logic by introducing epistemic modalities and proposing a new Fuzzy Computation Tree Logic of Knowledge(FCTLK).We represent fuzzy multi-agent systems as distributed knowledge bases with fuzzy epistemic interpreted systems.In addition,we provide a transformation algorithm from fuzzy epistemic interpreted systems to fuzzy Kripke structures,as well as transformation rules from FCTLK formulas to Fuzzy Computation Tree Logic(FCTL)formulas.Accordingly,we transform the FCTLK model checking problem into the FCTL model checking.This enables the verification of FCTLK formulas by using the fuzzy model checking algorithm of FCTL without additional computational overheads.Finally,we present correctness proofs and complexity analyses of the proposed algorithms.Additionally,we further illustrate the practical application of our approach through an example of a train control system.
文摘In this study, we are first examining well-known approach to improve fuzzy reasoning model (FRM) by use of the genetic-based learning mechanism [1]. Later we propose our alternative way to build FRM, which has significant precision advantages and does not require any adjustment/learning. We put together neuro-fuzzy system (NFS) to connect the set of exemplar input feature vectors (FV) with associated output label (target), both represented by their membership functions (MF). Next unknown FV would be classified by getting upper value of current output MF. After that the fuzzy truths for all MF upper values are maximized and the label of the winner is considered as the class of the input FV. We use the knowledge in the exemplar-label pairs directly with no training. It sets up automatically and then classifies all input FV from the same population as the exemplar FVs. We show that our approach statistically is almost twice as accurate, as well-known genetic-based learning mechanism FRM.
基金This work was supported by the National Natural Science Foundation of China （Grant Nos. 60273087 and 60474022）the Zhejiang Provincial Natural Science Foundation of China （Grant No. Y605389）.
文摘Based on the Schweizer-Sklar t-norm, a fuzzy logic system UL^＊ is established, and its soundness theorem and completeness theorem are proved. The following facts are pointed out＂ the well-known formal system SBL~ is a semantic extension of UL^＊; the fuzzy logic system IMTL△ is a special case of UL^＊ when two negations in UL^＊ coincide. Moreover, the connections between the system UL^＊ and some fuzzy logic formal systems are investigated. Finally, starting from the concepts of ＂the strength of an‘AND＇ operator＂ by R.R. Yager and ＂the strength of fuzzy rule interaction＂ by T. Whalen, the essential meaning of a parameter p in UL^＊ is explained and the use of fuzzy logic system UL^＊ in approximate reasoning is presented.
文摘The paper deals with the application of Volterra bound Interval type−2 fuzzy logic techniques in power quality assessment.This work proposes a new layout for detection,localization and classification of various types of power quality events.The proposed method exploits Volterra series for the extraction of relevant features,which are used to recognize different PQ events by Interval type-2 fuzzy logic based classifier.Numerous single as well as multiple powers signal disturbances have been simulated to testify the efficiency of the proposed technique.This time–frequency analysis results in the clear visual detection,localization,and classification of the different power quality events.The simulation results signify that the proposed scheme has a higher recognition rate while classifying single and multiple power quality events unlike other methods.Finally,the proposed method is compared with SVM,feed forward neural network and type−1 Fuzzy logic system based classifier to show the efficacy of the proposed technique in classifying the Power quality events.
基金supported by the Project Program of KLGHEI of China[2013CXZDA015]National Science Foundation of Guangdong Province[S2013010015768]+2 种基金Youth Program of Chongqing Three Gorges University[14QN30]Scientific,Technological Research Program of Chongqing Municipal Education Commission[KJ1401029]National Science Foundation of China[61273219].
文摘For a class of chaotic systems with unknown functions and disturbances,asymptotic stabilisation of the chaotic systems is achieved by designing a stability controller based on the adaptive fuzzy logic systems.At first,based on the universal approximation property of fuzzy logic systems,the Mamdani-type fuzzy logic systems with the parameter adaptive laws are designed utilising the data information sampled from the inputs and outputs of unknown functions in the chaotic systems,then the fuzzy logic systems are used to design the stability controller with three parameter adaptive laws,but the three parameters have no relationship with the number of fuzzy rules,so the stability controller is not only able to achieve asymptotic stabilisation for the chaotic system’s states,but also to reduce the number of fuzzy rules and the no-line computational burden significantly.Finally,simulations are used to show the validity of the stabilisation method.
文摘This study aims to predict the undrained shear strength of remolded soil samples using non-linear regression analyses,fuzzy logic,and artificial neural network modeling.A total of 1306 undrained shear strength results from 230 different remolded soil test settings reported in 21 publications were collected,utilizing six different measurement devices.Although water content,plastic limit,and liquid limit were used as input parameters for fuzzy logic and artificial neural network modeling,liquidity index or water content ratio was considered as an input parameter for non-linear regression analyses.In non-linear regression analyses,12 different regression equations were derived for the prediction of undrained shear strength of remolded soil.Feed-Forward backpropagation and the TANSIG transfer function were used for artificial neural network modeling,while the Mamdani inference system was preferred with trapezoidal and triangular membership functions for fuzzy logic modeling.The experimental results of 914 tests were used for training of the artificial neural network models,196 for validation and 196 for testing.It was observed that the accuracy of the artificial neural network and fuzzy logic modeling was higher than that of the non-linear regression analyses.Furthermore,a simple and reliable regression equation was proposed for assessments of undrained shear strength values with higher coefficients of determination.
文摘Increasing Internet of Things(IoT)device connectivity makes botnet attacks more dangerous,carrying catastrophic hazards.As IoT botnets evolve,their dynamic and multifaceted nature hampers conventional detection methods.This paper proposes a risk assessment framework based on fuzzy logic and Particle Swarm Optimization(PSO)to address the risks associated with IoT botnets.Fuzzy logic addresses IoT threat uncertainties and ambiguities methodically.Fuzzy component settings are optimized using PSO to improve accuracy.The methodology allows for more complex thinking by transitioning from binary to continuous assessment.Instead of expert inputs,PSO data-driven tunes rules and membership functions.This study presents a complete IoT botnet risk assessment system.The methodology helps security teams allocate resources by categorizing threats as high,medium,or low severity.This study shows how CICIoT2023 can assess cyber risks.Our research has implications beyond detection,as it provides a proactive approach to risk management and promotes the development of more secure IoT environments.
基金Key R&D Program of Xizang Autonomous Region(XZ202101ZY0004G)National Natural Science Foundation of China(U2142202)+1 种基金National Key R&D Program of China(2022YFC3004104)Key Innovation Team of China Meteor-ological Administration(CMA2022ZD07)。
文摘Short-duration heavy rainfall(SHR),as delineated by the National Meteorological Center of the China Me-teorological Administration,is characterized by hourly rainfall amounts no less than 20.0 mm.SHR is one of the most common convective weather phenomena that can cause severe damage.Short-range forecasting of SHR is an important part of operational severe weather prediction.In the present study,an improved objective SHR forecasting scheme was developed by adopting the ingredients-based methodology and using the fuzzy logic approach.The 1.0°×1.0°National Centers for Environmental Prediction(NCEP)final analysis data and the ordinary rainfall(0.1-19.9 mm h-1)and SHR observational data from 411 stations were used in the improved scheme.The best lifted index,the total precipitable water,the 925 hPa specific humidity(Q 925),and the 925 hPa divergence(DIV 925)were selected as predictors based on objective analysis.Continuously distributed membership functions of predictors were obtained based on relative frequency analysis.The weights of predictors were also objectively determined.Experiments with a typhoon SHR case and a spring SHR case show that the main possible areas could be captured by the improved scheme.Verification of SHR forecasts within 96 hours with NCEP global forecasts 1.0°×1.0°data initiated at 08:00 Beijing Time during the warm seasons in 2015 show the results were improved from both deterministic and probabilistic perspectives.This study provides an objectively feasible choice for short-range guidance forecasts of SHR.The scheme can be applied to other convective phenomena.
文摘The position control system of an electro-hydraulic actuator system （EHAS） is investigated in this paper. The EHAS is developed by taking into consideration the nonlinearities of the system： the friction and the internal leakage. A variable load that simulates a realistic load in robotic excavator is taken as the trajectory reference. A method of control strategy that is implemented by employing a fuzzy logic controller （FLC） whose parameters are optimized using particle swarm optimization （PSO） is proposed. The scaling factors of the fuzzy inference system are tuned to obtain the optimal values which yield the best system performance. The simulation results show that the FLC is able to track the trajectory reference accurately for a range of values of orifice opening. Beyond that range, the orifice opening may introduce chattering, which the FLC alone is not sufficient to overcome. The PSO optimized FLC can reduce the chattering significantly. This result justifies the implementation of the proposed method in position control of EHAS.
基金Project(90820302) supported by the National Natural Science Foundation of ChinaProject(20110491272) supported by China Postdoctoral Science Foundation of China+2 种基金Project(2012QNZT060) supported by the Fundamental Research Fund for the Central Universities of ChinaProject(11B070) supported by the Science Research Foundation of Education Bureau of Hunan Province,ChinaProject(2010-2012) supported by the Postdoctoral Science Foundation of Central South University,China
文摘To deal with fault detection and diagnosis with incomplete model for dead reckoning system of mobile robot,an integrative framework of particle filter detection and fuzzy logic diagnosis was devised.Firstly,an adaptive fault space is designed for recognizing both known faults and unknown faults,in corresponding modes of modeled and model-free.Secondly,the particle filter is utilized to diagnose the modeled faults and detect model-free fault according to the low particle weight and reliability.Especially,the proposed fuzzy logic diagnosis can further analyze model-free modes and identify some soft faults in unknown fault space.The MORCS-1 experimental results show that the fuzzy diagnosis particle filter(FDPF) combinational framework improves fault detection and identification completeness.Specifically speaking,FDPF is feasible to diagnose the modeled faults in known space.Furthermore,the types of model-free soft faults can also be further identified and diagnosed in unknown fault space.
文摘One of the most dangerous safety hazard in underground coal mines is roof falls during retreat mining.Roof falls may cause life-threatening and non-fatal injuries to miners and impede mining and transportation operations.As a result,a reliable roof fall prediction model is essential to tackle such challenges.Different parameters that substantially impact roof falls are ill-defined and intangible,making this an uncertain and challenging research issue.The National Institute for Occupational Safety and Health assembled a national database of roof performance from 37 coal mines to explore the factors contributing to roof falls.Data acquired for 37 mines is limited due to several restrictions,which increased the likelihood of incompleteness.Fuzzy logic is a technique for coping with ambiguity,incompleteness,and uncertainty.Therefore,In this paper,the fuzzy inference method is presented,which employs a genetic algorithm to create fuzzy rules based on 109 records of roof fall data and pattern search to refine the membership functions of parameters.The performance of the deployed model is evaluated using statistical measures such as the Root-Mean-Square Error,Mean-Absolute-Error,and coefficient of determination(R_(2)).Based on these criteria,the suggested model outperforms the existing models to precisely predict roof fall rates using fewer fuzzy rules.
