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
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 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
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
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
According to the randomness and uncertainty of information in the safety diagnosis of coal mine production system (CMPS), a novel safety diagnosis method was proposed by applying fuzzy logic inference method, which co...According to the randomness and uncertainty of information in the safety diagnosis of coal mine production system (CMPS), a novel safety diagnosis method was proposed by applying fuzzy logic inference method, which consists of safety diagnosis fuzzifier, defuzzifier, fuzzy rules base and inference engine. Through the safety diagnosis on coal mine roadway rail transportation system, the result shows that the unsafe probability is about 0.5 influenced by no speed reduction and over quick turnout on roadway, which is the most possible reason leading to the accident of roadway rail transportation system.展开更多
In several countries,the ageing population contour focuses on high healthcare costs and overloaded health care environments.Pervasive health care monitoring system can be a potential alternative,especially in the COVI...In several countries,the ageing population contour focuses on high healthcare costs and overloaded health care environments.Pervasive health care monitoring system can be a potential alternative,especially in the COVID-19 pandemic situation to help mitigate such problems by encouraging healthcare to transition from hospital-centred services to self-care,mobile care and home care.In this aspect,we propose a pervasive system to monitor the COVID’19 patient’s conditions within the hospital and outside by monitoring their medical and psychological situation.It facilitates better healthcare assistance,especially for COVID’19 patients and quarantined people.It identies the patient’s medical and psychological condition based on the current context and activities using a fuzzy context-aware reasoning engine based model.Fuzzy reasoning engine makes decisions using linguistic rules based on inference mechanisms that support the patient condition identication.Linguistics rules are framed based on the fuzzy set attributes belong to different context types.The fuzzy semantic rules are used to identify the relationship among the attributes,and the reasoning engine is used to ensure precise real-time context interpretation and current evaluation of the situation.Outcomes are measured using a fuzzy logic-based context reasoning system under simulation.The results indicate the usefulness of monitoring the COVID’19 patients based on the current context.展开更多
Objective To correct the nonlinear error of sensor output,a new approach to sensor inverse modeling based on Back-Propagation Fuzzy Logical System(BP FS) is presented.Methods The BP FS is a computationally efficient n...Objective To correct the nonlinear error of sensor output,a new approach to sensor inverse modeling based on Back-Propagation Fuzzy Logical System(BP FS) is presented.Methods The BP FS is a computationally efficient nonlinear universal approximator,which is capable of implementing complex nonlinear mapping from its input pattern space to the output with fast convergence speed.Results The neuro-fuzzy hybrid system,i.e.BP FS,is then applied to construct nonlinear inverse model of pressure sensor.The experimental results show that the proposed inverse modeling method automatically compensates the associated nonlinear error in pressure estimation,and thus the performance of pressure sensor is significantly improved.Conclusion The proposed method can be widely used in nonlinearity correction of various kinds of sensors to compensate the effects of nonlinearity and temperature on sensor output.展开更多
In this study an indirect adaptive sliding mode control (SMC) based on a fuzzy logic scheme is proposed to strengthen the tracking control performance of a general class of multi-input multi-output (MIMO) nonlinear un...In this study an indirect adaptive sliding mode control (SMC) based on a fuzzy logic scheme is proposed to strengthen the tracking control performance of a general class of multi-input multi-output (MIMO) nonlinear uncertain systems. Combining reaching law approach and fuzzy universal approximation theorem, the proposed design procedure combines the advantages of fuzzy logic control, adaptive control and sliding mode control. The stability of the control systems is proved in the sense of the Lyapunov second stability theorem. Two simulation studies are presented to demonstrate the effectiveness of our new hybrid control algorithm.展开更多
Fuzzy logic controller adopting unevenly-distributed membership function was presented with the purpose of enhancing performance of the temperature control precision and robustness for the chamber cooling system.Histo...Fuzzy logic controller adopting unevenly-distributed membership function was presented with the purpose of enhancing performance of the temperature control precision and robustness for the chamber cooling system.Histogram equalization and noise detection were performed to modify the evenly-distributed membership functions of error and error change rate into unevenly-distributed membership functions.Then,the experimental results with evenly and unevenly distributed membership functions were compared under the same outside environment conditions.The experimental results show that the steady-state error is reduced around 40% and the noise disturbance is rejected successfully even though noise range is 60% of the control precision range.The control precision is improved by reducing the steady-state error and the robustness is enhanced by rejecting noise disturbance through the fuzzy logic controller with unevenly-distributed membership function.Moreover,the system energy efficiency and lifetime of electronic expansion valve(EEV) installed in chamber cooling system are improved by adopting the unevenly-distributed membership function.展开更多
The spraying of plant protection product(PPP)in orchards is a very hazardous working procedure,owing to the spray drift caused by the uneven operation of conventional axial boom sprayers.This research describes an int...The spraying of plant protection product(PPP)in orchards is a very hazardous working procedure,owing to the spray drift caused by the uneven operation of conventional axial boom sprayers.This research describes an intelligent automated system for precise(PPP)distribution in real-time.It is based on an intelligent decision-making model using ultrasonic measurements of leaf area density under laboratory conditions,which serve to trigger electromagnetic valves(EMV)on the axial boom sprayer.A fuzzy logic algorithm was an integrated part of the intelligent system for controlling the PPP by generating the pulse width modulation signal and applying it through the EMV of the prototype boom sprayer.The results showed that by using an intelligent decision-making model,the same efficiency as with conventional methods could be achieved,but with reduced usage of plant protection products.Thus,the intelligent automated system used 4.8 times less spray mixture than the conventional one.展开更多
Every 24 seconds,someone dies on the road due to road accidents and it is the 8th leading cause of death and the first among children aged 15–29 years.1.35 million people globally die every year due to road traffic c...Every 24 seconds,someone dies on the road due to road accidents and it is the 8th leading cause of death and the first among children aged 15–29 years.1.35 million people globally die every year due to road traffic crashes.An additional 20–50 million suffer from non-fatal injuries,often resulting in longterm disabilities.This costs around 3%of Gross Domestic Product to most countries,and it is a considerable economic loss.The governments have taken various measures such as better road infrastructures and strict enforcement of motor-vehicle laws to reduce these accidents.However,there is still no remarkable reduction in the number of accidents.To ensure driver safety and achieve vision of zero accidents,there is a great need to monitor drivers’driving styles.Most of the existing driving behavior monitoring solutions are based on expensive hardware sensors.As most people are using smartphones in the modern era,a system based on mobile application is proposed,which can reduce the cost for developing intelligent transport systems(ITS)to a large extent.In this paper,we utilize the accelerometer sensor data and the global positioning system(GPS)sensor deployed in smartphones to recognize driving and speeding events.A driving style recognition system based on fuzzy logic is designed to classify different driving styles and control reckless driving by taking the longitudinal/lateral acceleration and speed as input parameters.Thus,the proposed system uses fuzzy logic rather than taking the crisp values of the sensors.Results indicate that the proposed system can classify reckless driving based on fuzzy logic and,therefore,reduce the number of accidents.展开更多
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^*.
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.
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.
基金Project(2006BAK04B0302)supported by the National Science and Technology Pillar Program during the 11th Five-year Plan of China
文摘According to the randomness and uncertainty of information in the safety diagnosis of coal mine production system (CMPS), a novel safety diagnosis method was proposed by applying fuzzy logic inference method, which consists of safety diagnosis fuzzifier, defuzzifier, fuzzy rules base and inference engine. Through the safety diagnosis on coal mine roadway rail transportation system, the result shows that the unsafe probability is about 0.5 influenced by no speed reduction and over quick turnout on roadway, which is the most possible reason leading to the accident of roadway rail transportation system.
基金funding by the University of Malta’s Internal Research Grants。
文摘In several countries,the ageing population contour focuses on high healthcare costs and overloaded health care environments.Pervasive health care monitoring system can be a potential alternative,especially in the COVID-19 pandemic situation to help mitigate such problems by encouraging healthcare to transition from hospital-centred services to self-care,mobile care and home care.In this aspect,we propose a pervasive system to monitor the COVID’19 patient’s conditions within the hospital and outside by monitoring their medical and psychological situation.It facilitates better healthcare assistance,especially for COVID’19 patients and quarantined people.It identies the patient’s medical and psychological condition based on the current context and activities using a fuzzy context-aware reasoning engine based model.Fuzzy reasoning engine makes decisions using linguistic rules based on inference mechanisms that support the patient condition identication.Linguistics rules are framed based on the fuzzy set attributes belong to different context types.The fuzzy semantic rules are used to identify the relationship among the attributes,and the reasoning engine is used to ensure precise real-time context interpretation and current evaluation of the situation.Outcomes are measured using a fuzzy logic-based context reasoning system under simulation.The results indicate the usefulness of monitoring the COVID’19 patients based on the current context.
基金This work was supported by National Natural Science Foundation of China(No.60276037).
文摘Objective To correct the nonlinear error of sensor output,a new approach to sensor inverse modeling based on Back-Propagation Fuzzy Logical System(BP FS) is presented.Methods The BP FS is a computationally efficient nonlinear universal approximator,which is capable of implementing complex nonlinear mapping from its input pattern space to the output with fast convergence speed.Results The neuro-fuzzy hybrid system,i.e.BP FS,is then applied to construct nonlinear inverse model of pressure sensor.The experimental results show that the proposed inverse modeling method automatically compensates the associated nonlinear error in pressure estimation,and thus the performance of pressure sensor is significantly improved.Conclusion The proposed method can be widely used in nonlinearity correction of various kinds of sensors to compensate the effects of nonlinearity and temperature on sensor output.
文摘In this study an indirect adaptive sliding mode control (SMC) based on a fuzzy logic scheme is proposed to strengthen the tracking control performance of a general class of multi-input multi-output (MIMO) nonlinear uncertain systems. Combining reaching law approach and fuzzy universal approximation theorem, the proposed design procedure combines the advantages of fuzzy logic control, adaptive control and sliding mode control. The stability of the control systems is proved in the sense of the Lyapunov second stability theorem. Two simulation studies are presented to demonstrate the effectiveness of our new hybrid control algorithm.
文摘Fuzzy logic controller adopting unevenly-distributed membership function was presented with the purpose of enhancing performance of the temperature control precision and robustness for the chamber cooling system.Histogram equalization and noise detection were performed to modify the evenly-distributed membership functions of error and error change rate into unevenly-distributed membership functions.Then,the experimental results with evenly and unevenly distributed membership functions were compared under the same outside environment conditions.The experimental results show that the steady-state error is reduced around 40% and the noise disturbance is rejected successfully even though noise range is 60% of the control precision range.The control precision is improved by reducing the steady-state error and the robustness is enhanced by rejecting noise disturbance through the fuzzy logic controller with unevenly-distributed membership function.Moreover,the system energy efficiency and lifetime of electronic expansion valve(EEV) installed in chamber cooling system are improved by adopting the unevenly-distributed membership function.
基金This article was partly created as a result of the applied EUREKA project(No.3211-10-000040).
文摘The spraying of plant protection product(PPP)in orchards is a very hazardous working procedure,owing to the spray drift caused by the uneven operation of conventional axial boom sprayers.This research describes an intelligent automated system for precise(PPP)distribution in real-time.It is based on an intelligent decision-making model using ultrasonic measurements of leaf area density under laboratory conditions,which serve to trigger electromagnetic valves(EMV)on the axial boom sprayer.A fuzzy logic algorithm was an integrated part of the intelligent system for controlling the PPP by generating the pulse width modulation signal and applying it through the EMV of the prototype boom sprayer.The results showed that by using an intelligent decision-making model,the same efficiency as with conventional methods could be achieved,but with reduced usage of plant protection products.Thus,the intelligent automated system used 4.8 times less spray mixture than the conventional one.
文摘Every 24 seconds,someone dies on the road due to road accidents and it is the 8th leading cause of death and the first among children aged 15–29 years.1.35 million people globally die every year due to road traffic crashes.An additional 20–50 million suffer from non-fatal injuries,often resulting in longterm disabilities.This costs around 3%of Gross Domestic Product to most countries,and it is a considerable economic loss.The governments have taken various measures such as better road infrastructures and strict enforcement of motor-vehicle laws to reduce these accidents.However,there is still no remarkable reduction in the number of accidents.To ensure driver safety and achieve vision of zero accidents,there is a great need to monitor drivers’driving styles.Most of the existing driving behavior monitoring solutions are based on expensive hardware sensors.As most people are using smartphones in the modern era,a system based on mobile application is proposed,which can reduce the cost for developing intelligent transport systems(ITS)to a large extent.In this paper,we utilize the accelerometer sensor data and the global positioning system(GPS)sensor deployed in smartphones to recognize driving and speeding events.A driving style recognition system based on fuzzy logic is designed to classify different driving styles and control reckless driving by taking the longitudinal/lateral acceleration and speed as input parameters.Thus,the proposed system uses fuzzy logic rather than taking the crisp values of the sensors.Results indicate that the proposed system can classify reckless driving based on fuzzy logic and,therefore,reduce the number of accidents.
