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
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
A novel probabilistic fuzzy control system is proposed to treat the congestion avoidance problem in transmission control protocol （TCP） networks. Studies on traffic measurement of TCP networks have shown that the pa...A novel probabilistic fuzzy control system is proposed to treat the congestion avoidance problem in transmission control protocol （TCP） networks. Studies on traffic measurement of TCP networks have shown that the packet traffic exhibits long range dependent properties called self-similarity, which degrades the network performance greatly. The probabilistic fuzzy control （PFC） system is used to handle the complex stochastic features of self-similar traffic and the modeling uncertainties in the network system. A three-dimensional （3-D） membership function （MF） is embedded in the PFC to express and describe the stochastic feature of network traffic. The 3-D MF has extended the traditional fuzzy planar mapping and further provides a spatial mapping among ＂fuzziness-randomness-state＂. The additional stochastic expression of 3-D MF provides the PFC an additional freedom to handle the stochastic features of self-similar traffic. Simulation experiments show that the proposed control method achieves superior performance compared to traditional control schemes in a stochastic environment.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
Emotion Model is the basis of facial expression recognition system. The constructed emotional model should not only match facial expressions with emotions, but also reflect the location relationship between different ...Emotion Model is the basis of facial expression recognition system. The constructed emotional model should not only match facial expressions with emotions, but also reflect the location relationship between different emotions. In this way, it is easy to understand the current emotion of an individual through the analysis of the acquired facial expression information. This paper constructs an improved three-dimensional model for emotion based on fuzzy theory, which corresponds to the facial features to emotions based on the basic emotions proposed by Ekman. What’s more, the three-dimensional model for motion is able to divide every emotion into three different groups which can show the positional relationship visually and quantitatively and at the same time determine the degree of emotion based on fuzzy theory.展开更多
Our purpose in this study was to develop an automated method for measuring three-dimensional (3D) cerebral cortical thicknesses in patients with Alzheimer’s disease (AD) using magnetic resonance (MR) images. Our prop...Our purpose in this study was to develop an automated method for measuring three-dimensional (3D) cerebral cortical thicknesses in patients with Alzheimer’s disease (AD) using magnetic resonance (MR) images. Our proposed method consists of mainly three steps. First, a brain parenchymal region was segmented based on brain model matching. Second, a 3D fuzzy membership map for a cerebral cortical region was created by applying a fuzzy c-means (FCM) clustering algorithm to T1-weighted MR images. Third, cerebral cortical thickness was three- dimensionally measured on each cortical surface voxel by using a localized gradient vector trajectory in a fuzzy membership map. Spherical models with 3 mm artificial cortical regions, which were produced using three noise levels of 2%, 5%, and 10%, were employed to evaluate the proposed method. We also applied the proposed method to T1-weighted images obtained from 20 cases, i.e., 10 clinically diagnosed AD cases and 10 clinically normal (CN) subjects. The thicknesses of the 3 mm artificial cortical regions for spherical models with noise levels of 2%, 5%, and 10% were measured by the proposed method as 2.953 ± 0.342, 2.953 ± 0.342 and 2.952 ± 0.343 mm, respectively. Thus the mean thicknesses for the entire cerebral lobar region were 3.1 ± 0.4 mm for AD patients and 3.3 ± 0.4 mm for CN subjects, respectively (p < 0.05). The proposed method could be feasible for measuring the 3D cerebral cortical thickness on individual cortical surface voxels as an atrophy feature in AD.展开更多
To enable representation and reasoning for fuzzy ontologies with expressive fuzzy knowledge on the semantic web, a new fuzzy extension of description logics called the fuzzy description logics with comparison expressi...To enable representation and reasoning for fuzzy ontologies with expressive fuzzy knowledge on the semantic web, a new fuzzy extension of description logics called the fuzzy description logics with comparison expressions （FCDLs） is presented. The syntax and semantics of FCDLs are formally defined, and the forms of axioms and assertions in FCDLs knowledge bases are specified. FCDLs combine both fuzzy concepts from the fuzzy description logics （FDLs） and cut concepts from the extended fuzzy description logics （EFDLs） in the same theory. Furthermore, cut concepts are extended into comparison cut concepts in FCDLs to represent comparison expressions between fuzzy membership degrees, which are often used in practice but not supported by the other fuzzy extensions of description logics. FCDLs have more expressive power than FDLs and EFDLs, and are able to represent expressive fuzzy knowledge and to perform reasoning tasks based on them. Therefore, FCDLs can enable representation and reasoning for fuzzy ontologies with expressive fuzzy knowledge on the semantic web.展开更多
In order to improve a mobile robot＇s autonomy in unknown environments, a novel intelligent controller is designed. The proposed controller is based on fuzzy logic with the aim of assisting a multi-sensor equipped mob...In order to improve a mobile robot＇s autonomy in unknown environments, a novel intelligent controller is designed. The proposed controller is based on fuzzy logic with the aim of assisting a multi-sensor equipped mobile robot to safely navigate in an indoor environment. First, the designs of two behaviors for a robot＇s autonomous navigation are described, including path tracking and obstacle avoidance, which emulate human driving behaviors and reduce the complexity of the robot＇s navigation problems in unknown environments. Secondly, the two behaviors are combined by using a finite state machine （FSM）, which ensures that the robot can safely track a predefined path in an unknown indoor environment. The inputs to this controller are the readings from the sensors. The corresponding output is the desired direction of the robot. Finally, both the simulation and experimental results verify the effectiveness of the proposed method.展开更多
This paper proposes a Fuzzy Neural Network (FNN) model, which uses a propagation algorithm. A logical operation is defined by a set of weights which are independent of inputs. The realization of the basic And,Or and N...This paper proposes a Fuzzy Neural Network (FNN) model, which uses a propagation algorithm. A logical operation is defined by a set of weights which are independent of inputs. The realization of the basic And,Or and Negation fuzzy logical operations is shown by the fuzzy neuron. A example in fault diagnosis is put forward and the result witnesses some effectiveness of the new FNN model.展开更多
To enable the representation and reasoning for fuzzy ontologies with expressive fuzzy knowledge on the semantic web, a new fuzzy extension of description logics called vague ALC which is based on vague sets is present...To enable the representation and reasoning for fuzzy ontologies with expressive fuzzy knowledge on the semantic web, a new fuzzy extension of description logics called vague ALC which is based on vague sets is presented. The definition of vague set is introduced and then the syntax and semantics of vague ALC are formally defined. The forms of axioms and assertions in the vague ALC knowledge bases are specified. Finally, the tableau algorithm is developed for the reasoning in the vague ALC. The vague ALC based on vague set uses two degrees of membership instead of a single membership degree in the fuzzy sets and is more accurate in representing the imprecision in the degrees of membership. The vague ALC has more expressive power than ALC and can represent fuzzy knowledge and perform reasoning tasks based on them. Therefore, the vague ALC can enable the representation and reasoning for fuzzy ontologies with expressive fuzzy knowledge on the semantic web.展开更多
To solve the extended fuzzy description logic with qualifying number restriction （EFALCQ） reasoning problems, EFALCQ is discretely simulated by description logic with qualifying number restriction （ALCQ）, and ALCQ...To solve the extended fuzzy description logic with qualifying number restriction （EFALCQ） reasoning problems, EFALCQ is discretely simulated by description logic with qualifying number restriction （ALCQ）, and ALCQ reasoning results are reused to prove the complexity of EFALCQ reasoning problems. The ALCQ simulation method for the consistency of EFALCQ is proposed. This method reduces EFALCQ satisfiability into EFALCQ consistency, and uses EFALCQ satisfiability to discretely simulate EFALCQ satdomain. It is proved that the reasoning complexity for EFALCQ satisfiability, consistency and sat-domain is PSPACE-complete.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
A new real-time map matching algorithm based on fuzzy logic is proposed. 3 main factors affecting the reliability of map matching, including the distance between the vehicle location and the matching road segment, the...A new real-time map matching algorithm based on fuzzy logic is proposed. 3 main factors affecting the reliability of map matching, including the distance between the vehicle location and the matching road segment, the angle between the vehicle direction and the road segment direction and the road connectivity are discussed. Fuzzy rules for the distance, angle and connectivity are presented to calculate the matching reliability. 2 indicators for estimating the matching reliability are then derived, one is the lower limit of the reliability, and the other is the limit error of the difference between the maximal value and the second-maximal value of the reliability. A real-time map-matching system based on fuzzy logic is therefore developed. Using the real data of global positioning system(GIS) based navigation and geographic information system(GPS) based road map, the method is verified and the (results) prove the effectiveness of the proposed method.展开更多
A major issue in radar quantitative precipitation estimation is the contamination of radar echoes by non-meteorological targets such as ground clutter,chaff,clear air echoes etc.In this study,a fuzzy logic algorithm f...A major issue in radar quantitative precipitation estimation is the contamination of radar echoes by non-meteorological targets such as ground clutter,chaff,clear air echoes etc.In this study,a fuzzy logic algorithm for the identification of non-meteorological echoes is developed using optimized membership functions and weights for the dual-polarization radar located at Mount Sobaek.For selected precipitation and non-meteorological events,the characteristics of the precipitation and non-meteorological echo are derived by the probability density functions of five fuzzy parameters as functions of reflectivity values.The membership functions and weights are then determined by these density functions.Finally,the nonmeteorological echoes are identified by combining the membership functions and weights.The performance is qualitatively evaluated by long-term rain accumulation.The detection accuracy of the fuzzy logic algorithm is calculated using the probability of detection（POD）,false alarm rate（FAR）,and clutter–signal ratio（CSR）.In addition,the issues in using filtered dual-polarization data are alleviated.展开更多
The bucket wheel reclaimer(BWR) is a key piece of equipment which has been widely used for stacking and reclaiming bulk materials(i.e.iron ore and coal) in places such as ports,iron-steel plants,coal storage areas,and...The bucket wheel reclaimer(BWR) is a key piece of equipment which has been widely used for stacking and reclaiming bulk materials(i.e.iron ore and coal) in places such as ports,iron-steel plants,coal storage areas,and power stations from stockpiles.BWRs are very large in size,heavy in weight,expensive in price,and slow in motion.There are many challenges in attempting to automatically control their motion to accurately follow the required trajectories involving uncertain parameters from factors such as friction,turbulent wind,its own dynamics,and encoder limitations.As BWRs are always heavily engaged in production and cannot be spared very long for motion control studies and associated developments,a BWR model and simulation environment closely resembling real life conditions would be beneficial.The following research focused mainly on the implementation of fuzzy logic to a BWR motion control from an engineer's perspective.First,the modeling of a BWR including partially known parameters such as friction force and turbulence to the system was presented.This was then followed by the design of a fuzzy logic-based control built on a model-based control loop.The investigation provides engineers with an example of applying fuzzy logic in a model based approach to properly control the motion of a large BWR following defined trajectories,as well as to show possible ways of further improving the controller performance.The result indicates that fuzzy logic can be applied easily by engineers to overcome most motion control issues involving a large BWR.展开更多
An artificial neural network(ANN) and a self-adjusting fuzzy logiccontroller(FLC) for modeling and control of gas tungsten arc welding(GTAW) process are presented.The discussion is mainly focused on the modeling and c...An artificial neural network(ANN) and a self-adjusting fuzzy logiccontroller(FLC) for modeling and control of gas tungsten arc welding(GTAW) process are presented.The discussion is mainly focused on the modeling and control of the weld pool depth with ANN and theintelligent control for weld seam tracking with FLC. The proposed neural network can produce highlycomplex nonlinear multi-variable model of the GTAW process that offers the accurate prediction ofwelding penetration depth. A self-adjusting fuzzy controller used for seam tracking adjusts thecontrol parameters on-line automatically according to the tracking errors so that the torch positioncan be controlled accurately.展开更多
The target of this paper is the performance-based diagnostics of a gas turbine for the automated early detection of components malfunctions. The paper proposes a new combination of multiple methodologies for the perfo...The target of this paper is the performance-based diagnostics of a gas turbine for the automated early detection of components malfunctions. The paper proposes a new combination of multiple methodologies for the performance-based diagnostics of single and multiple failures on a two-spool engine. The aim of this technique is to combine the strength of each methodology and provide a high success rate for single and multiple failures with the presence of measurement malfunctions. A combination of KF(Kalman Filter), ANN(Artificial Neural Network) and FL(Fuzzy Logic) is used in this research in order to improve the success rate, to increase the flexibility and the number of failures detected and to combine the strength of multiple methods to have a more robust solution. The Kalman filter has in his strength the measurement noise treatment, the artificial neural network the simulation and prediction of reference and deteriorated performance profile and the fuzzy logic the categorization flexibility, which is used to quantify and classify the failures. In the area of GT(Gas Turbine) diagnostics, the multiple failures in combination with measurement issues and the utilization of multiple methods for a 2-spool industrial gas turbine engine has not been investigated extensively.This paper reports the key contribution of each component of the methodology and brief the results in the quantification and classification success rate. The methodology is tested for constant deterioration and increasing noise and for random deterioration. For the random deterioration and nominal noise of 0.4%, in particular, the quantification success rate is above 92.0%, while the classification success rate is above 95.1%. Moreover, the speed of the data processing(1.7 s/sample)proves the suitability of this methodology for online diagnostics.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supported by the National Natural Science Foundation of China (U0735003,60604006)Natural Science Foundation of Guangdong Province (8351009001000002,6021452)
文摘A novel probabilistic fuzzy control system is proposed to treat the congestion avoidance problem in transmission control protocol （TCP） networks. Studies on traffic measurement of TCP networks have shown that the packet traffic exhibits long range dependent properties called self-similarity, which degrades the network performance greatly. The probabilistic fuzzy control （PFC） system is used to handle the complex stochastic features of self-similar traffic and the modeling uncertainties in the network system. A three-dimensional （3-D） membership function （MF） is embedded in the PFC to express and describe the stochastic feature of network traffic. The 3-D MF has extended the traditional fuzzy planar mapping and further provides a spatial mapping among ＂fuzziness-randomness-state＂. The additional stochastic expression of 3-D MF provides the PFC an additional freedom to handle the stochastic features of self-similar traffic. Simulation experiments show that the proposed control method achieves superior performance compared to traditional control schemes in a stochastic environment.
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.
文摘Emotion Model is the basis of facial expression recognition system. The constructed emotional model should not only match facial expressions with emotions, but also reflect the location relationship between different emotions. In this way, it is easy to understand the current emotion of an individual through the analysis of the acquired facial expression information. This paper constructs an improved three-dimensional model for emotion based on fuzzy theory, which corresponds to the facial features to emotions based on the basic emotions proposed by Ekman. What’s more, the three-dimensional model for motion is able to divide every emotion into three different groups which can show the positional relationship visually and quantitatively and at the same time determine the degree of emotion based on fuzzy theory.
文摘Our purpose in this study was to develop an automated method for measuring three-dimensional (3D) cerebral cortical thicknesses in patients with Alzheimer’s disease (AD) using magnetic resonance (MR) images. Our proposed method consists of mainly three steps. First, a brain parenchymal region was segmented based on brain model matching. Second, a 3D fuzzy membership map for a cerebral cortical region was created by applying a fuzzy c-means (FCM) clustering algorithm to T1-weighted MR images. Third, cerebral cortical thickness was three- dimensionally measured on each cortical surface voxel by using a localized gradient vector trajectory in a fuzzy membership map. Spherical models with 3 mm artificial cortical regions, which were produced using three noise levels of 2%, 5%, and 10%, were employed to evaluate the proposed method. We also applied the proposed method to T1-weighted images obtained from 20 cases, i.e., 10 clinically diagnosed AD cases and 10 clinically normal (CN) subjects. The thicknesses of the 3 mm artificial cortical regions for spherical models with noise levels of 2%, 5%, and 10% were measured by the proposed method as 2.953 ± 0.342, 2.953 ± 0.342 and 2.952 ± 0.343 mm, respectively. Thus the mean thicknesses for the entire cerebral lobar region were 3.1 ± 0.4 mm for AD patients and 3.3 ± 0.4 mm for CN subjects, respectively (p < 0.05). The proposed method could be feasible for measuring the 3D cerebral cortical thickness on individual cortical surface voxels as an atrophy feature in AD.
基金The National Natural Science Foundation of China(No.60373066,60425206,90412003),the National Basic Research Pro-gram of China (973Program)(No.2002CB312000),the Innovation Plan for Jiangsu High School Graduate Student, the High TechnologyResearch Project of Jiangsu Province (No.BG2005032), and the Weap-onry Equipment Foundation of PLA Equipment Ministry ( No.51406020105JB8103).
文摘To enable representation and reasoning for fuzzy ontologies with expressive fuzzy knowledge on the semantic web, a new fuzzy extension of description logics called the fuzzy description logics with comparison expressions （FCDLs） is presented. The syntax and semantics of FCDLs are formally defined, and the forms of axioms and assertions in FCDLs knowledge bases are specified. FCDLs combine both fuzzy concepts from the fuzzy description logics （FDLs） and cut concepts from the extended fuzzy description logics （EFDLs） in the same theory. Furthermore, cut concepts are extended into comparison cut concepts in FCDLs to represent comparison expressions between fuzzy membership degrees, which are often used in practice but not supported by the other fuzzy extensions of description logics. FCDLs have more expressive power than FDLs and EFDLs, and are able to represent expressive fuzzy knowledge and to perform reasoning tasks based on them. Therefore, FCDLs can enable representation and reasoning for fuzzy ontologies with expressive fuzzy knowledge on the semantic web.
基金Cultivation Fund for Innovation Project of Ministry of Education (No.708045)
文摘In order to improve a mobile robot＇s autonomy in unknown environments, a novel intelligent controller is designed. The proposed controller is based on fuzzy logic with the aim of assisting a multi-sensor equipped mobile robot to safely navigate in an indoor environment. First, the designs of two behaviors for a robot＇s autonomous navigation are described, including path tracking and obstacle avoidance, which emulate human driving behaviors and reduce the complexity of the robot＇s navigation problems in unknown environments. Secondly, the two behaviors are combined by using a finite state machine （FSM）, which ensures that the robot can safely track a predefined path in an unknown indoor environment. The inputs to this controller are the readings from the sensors. The corresponding output is the desired direction of the robot. Finally, both the simulation and experimental results verify the effectiveness of the proposed method.
文摘This paper proposes a Fuzzy Neural Network (FNN) model, which uses a propagation algorithm. A logical operation is defined by a set of weights which are independent of inputs. The realization of the basic And,Or and Negation fuzzy logical operations is shown by the fuzzy neuron. A example in fault diagnosis is put forward and the result witnesses some effectiveness of the new FNN model.
基金Program for New Century Excellent Talents in Uni-versity (NoNCET-05-0288)
文摘To enable the representation and reasoning for fuzzy ontologies with expressive fuzzy knowledge on the semantic web, a new fuzzy extension of description logics called vague ALC which is based on vague sets is presented. The definition of vague set is introduced and then the syntax and semantics of vague ALC are formally defined. The forms of axioms and assertions in the vague ALC knowledge bases are specified. Finally, the tableau algorithm is developed for the reasoning in the vague ALC. The vague ALC based on vague set uses two degrees of membership instead of a single membership degree in the fuzzy sets and is more accurate in representing the imprecision in the degrees of membership. The vague ALC has more expressive power than ALC and can represent fuzzy knowledge and perform reasoning tasks based on them. Therefore, the vague ALC can enable the representation and reasoning for fuzzy ontologies with expressive fuzzy knowledge on the semantic web.
基金The National Natural Science Foundation of China(No60403016)the Weaponry Equipment Foundation of PLA Equip-ment Ministry (No51406020105JB8103)
文摘To solve the extended fuzzy description logic with qualifying number restriction （EFALCQ） reasoning problems, EFALCQ is discretely simulated by description logic with qualifying number restriction （ALCQ）, and ALCQ reasoning results are reused to prove the complexity of EFALCQ reasoning problems. The ALCQ simulation method for the consistency of EFALCQ is proposed. This method reduces EFALCQ satisfiability into EFALCQ consistency, and uses EFALCQ satisfiability to discretely simulate EFALCQ satdomain. It is proved that the reasoning complexity for EFALCQ satisfiability, consistency and sat-domain is PSPACE-complete.
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Projects(40301043 and 40171078) supported by the National Natural Science Foundation of China
文摘A new real-time map matching algorithm based on fuzzy logic is proposed. 3 main factors affecting the reliability of map matching, including the distance between the vehicle location and the matching road segment, the angle between the vehicle direction and the road segment direction and the road connectivity are discussed. Fuzzy rules for the distance, angle and connectivity are presented to calculate the matching reliability. 2 indicators for estimating the matching reliability are then derived, one is the lower limit of the reliability, and the other is the limit error of the difference between the maximal value and the second-maximal value of the reliability. A real-time map-matching system based on fuzzy logic is therefore developed. Using the real data of global positioning system(GIS) based navigation and geographic information system(GPS) based road map, the method is verified and the (results) prove the effectiveness of the proposed method.
基金supported by a grant(14AWMP-B079364-01) from Water Management Research Program funded by Ministry of Land,Infrastructure and Transport of Korean government
文摘A major issue in radar quantitative precipitation estimation is the contamination of radar echoes by non-meteorological targets such as ground clutter,chaff,clear air echoes etc.In this study,a fuzzy logic algorithm for the identification of non-meteorological echoes is developed using optimized membership functions and weights for the dual-polarization radar located at Mount Sobaek.For selected precipitation and non-meteorological events,the characteristics of the precipitation and non-meteorological echo are derived by the probability density functions of five fuzzy parameters as functions of reflectivity values.The membership functions and weights are then determined by these density functions.Finally,the nonmeteorological echoes are identified by combining the membership functions and weights.The performance is qualitatively evaluated by long-term rain accumulation.The detection accuracy of the fuzzy logic algorithm is calculated using the probability of detection（POD）,false alarm rate（FAR）,and clutter–signal ratio（CSR）.In addition,the issues in using filtered dual-polarization data are alleviated.
基金support through the ARC Linkage LP0989780 grant titled "The study anddevelopment of a 3-D real-time stockpile management system"the support in part from Institute for Mineral and Energy Resources,University of Adelaide 2009-2010,as well as Faculty of Engineering,Computer and Mathematical Sciences strategic research funding,2010
文摘The bucket wheel reclaimer(BWR) is a key piece of equipment which has been widely used for stacking and reclaiming bulk materials(i.e.iron ore and coal) in places such as ports,iron-steel plants,coal storage areas,and power stations from stockpiles.BWRs are very large in size,heavy in weight,expensive in price,and slow in motion.There are many challenges in attempting to automatically control their motion to accurately follow the required trajectories involving uncertain parameters from factors such as friction,turbulent wind,its own dynamics,and encoder limitations.As BWRs are always heavily engaged in production and cannot be spared very long for motion control studies and associated developments,a BWR model and simulation environment closely resembling real life conditions would be beneficial.The following research focused mainly on the implementation of fuzzy logic to a BWR motion control from an engineer's perspective.First,the modeling of a BWR including partially known parameters such as friction force and turbulence to the system was presented.This was then followed by the design of a fuzzy logic-based control built on a model-based control loop.The investigation provides engineers with an example of applying fuzzy logic in a model based approach to properly control the motion of a large BWR following defined trajectories,as well as to show possible ways of further improving the controller performance.The result indicates that fuzzy logic can be applied easily by engineers to overcome most motion control issues involving a large BWR.
基金National Natural Science Foundation of China and Provincial Natural Science Foundafion of Guangdong, China.
文摘An artificial neural network(ANN) and a self-adjusting fuzzy logiccontroller(FLC) for modeling and control of gas tungsten arc welding(GTAW) process are presented.The discussion is mainly focused on the modeling and control of the weld pool depth with ANN and theintelligent control for weld seam tracking with FLC. The proposed neural network can produce highlycomplex nonlinear multi-variable model of the GTAW process that offers the accurate prediction ofwelding penetration depth. A self-adjusting fuzzy controller used for seam tracking adjusts thecontrol parameters on-line automatically according to the tracking errors so that the torch positioncan be controlled accurately.
文摘The target of this paper is the performance-based diagnostics of a gas turbine for the automated early detection of components malfunctions. The paper proposes a new combination of multiple methodologies for the performance-based diagnostics of single and multiple failures on a two-spool engine. The aim of this technique is to combine the strength of each methodology and provide a high success rate for single and multiple failures with the presence of measurement malfunctions. A combination of KF(Kalman Filter), ANN(Artificial Neural Network) and FL(Fuzzy Logic) is used in this research in order to improve the success rate, to increase the flexibility and the number of failures detected and to combine the strength of multiple methods to have a more robust solution. The Kalman filter has in his strength the measurement noise treatment, the artificial neural network the simulation and prediction of reference and deteriorated performance profile and the fuzzy logic the categorization flexibility, which is used to quantify and classify the failures. In the area of GT(Gas Turbine) diagnostics, the multiple failures in combination with measurement issues and the utilization of multiple methods for a 2-spool industrial gas turbine engine has not been investigated extensively.This paper reports the key contribution of each component of the methodology and brief the results in the quantification and classification success rate. The methodology is tested for constant deterioration and increasing noise and for random deterioration. For the random deterioration and nominal noise of 0.4%, in particular, the quantification success rate is above 92.0%, while the classification success rate is above 95.1%. Moreover, the speed of the data processing(1.7 s/sample)proves the suitability of this methodology for online diagnostics.
