Fault detection and diagnosis for pneumatic system of automatic productionline are studied. An expert system using fuzzy-neural network and pneumatic circuit fault diagnosisinstrument are deigned. The mathematical mod...Fault detection and diagnosis for pneumatic system of automatic productionline are studied. An expert system using fuzzy-neural network and pneumatic circuit fault diagnosisinstrument are deigned. The mathematical model of various pneumatic faults and experimental deviceare built. In the end, some experiments are done, which shows that the expert system usingfuzzy-neural network can diagnose fast and truly fault of pneumatic circuit.展开更多
This paper presents an improved design for the hypersonic reentry vehicle(HRV) by the trajectory linearization control(TLC) technology for the design of HRV. The physics-based model fails to take into account the exte...This paper presents an improved design for the hypersonic reentry vehicle(HRV) by the trajectory linearization control(TLC) technology for the design of HRV. The physics-based model fails to take into account the external disturbance in the flight envelope in which the stability and control derivatives prove to be nonlinear and time-varying, which is likely in turn to increase the difficulty in keeping the stability of the attitude control system. Therefore, it is of great significance to modulate the unsteady and nonlinear characteristic features of the system parameters so as to overcome the disadvantages of the conventional TLC technology that can only be valid and efficient in the cases when there may exist any minor uncertainties. It is just for this kind of necessity that we have developed a fuzzy-neural disturbance observer(FNDO) based on the B-spline to estimate such uncertainties and disturbances concerned by establishing a new dynamic system. The simulation results gained by using the aforementioned technology and the observer show that it is just due to the innovation of the adaptive trajectory linearization control(ATLC) system. Significant improvement has been realized in the performance and the robustness of the system in addition to its fault tolerance.展开更多
Non-linearity and parameter time-variety are inherent properties of lateral motions of a vehicle. How to effectively control intelligent vehicle (IV) lateral motions is a challenging task. Controller design can be reg...Non-linearity and parameter time-variety are inherent properties of lateral motions of a vehicle. How to effectively control intelligent vehicle (IV) lateral motions is a challenging task. Controller design can be regarded as a process of searching optimal structure from controller structure space and searching optimal parameters from parameter space. Based on this view, an intelligent vehicle lateral motions controller was designed. The controller structure was constructed by T-S fuzzy-neural network (FNN). Its parameters were searched and selected with genetic algorithm (GA). The simulation results indicate that the controller designed has strong robustness, high precision and good ride quality, and it can effectively resolve IV lateral motion non-linearity and time-variant parameters problem.展开更多
The paper describes results obtained in the development of adaptive fuzzy-neural navigation subsystem for mobile legged robot. In order to keep the motion sufficiently smooth, free of sharp turnings and transversal sw...The paper describes results obtained in the development of adaptive fuzzy-neural navigation subsystem for mobile legged robot. In order to keep the motion sufficiently smooth, free of sharp turnings and transversal swings when moving between closely located obstacles, fuzzy rules are updated on-line. To this end, the fuzzy rules are expressed through a layered feed-forward neural network and parameters are updated on line in two steps--the rough and fine updating. That is followed by the description of the learning fault diagnosis using binary neural network based on the Carpenter and Grossbergs＇ adaptive resonance theory.展开更多
Purpose–The two main purposes of this paper are:first,the development of a new optimization algorithm called GHSACO by incorporating the global-best harmony search(GHS)which is a stochastic optimization algorithm rec...Purpose–The two main purposes of this paper are:first,the development of a new optimization algorithm called GHSACO by incorporating the global-best harmony search(GHS)which is a stochastic optimization algorithm recently developed,with the ant colony optimization(ACO)algorithm.Second,design of a new indirect adaptive recurrent fuzzy-neural controller(IARFNNC)for uncertain nonlinear systems using the developed optimization method(GHSACO)and the concept of the supervisory controller.Design/methodology/approach–The novel optimization method introduces a novel improvization process,which is different from that of the GHS in the following aspects:a modified harmony memory representation and conception.The use of a global random switching mechanism to monitor the choice between the ACO and GHS.An additional memory consideration selection rule using the ACO random proportional transition rule with a pheromone trail update mechanism.The developed optimization method is applied for parametric optimization of all recurrent fuzzy neural networks adaptive controller parameters.In addition,in order to guarantee that the system states are confined to the safe region,a supervisory controller is incorporated into the IARFNNC global structure.Findings–First,to analyze the performance of GHSACO method and shows its effectiveness,some benchmark functions with different dimensions are used.Simulation results demonstrate that it can find significantly better solutions when compared with the Harmony Search(HS),GHS,improved HS(IHS)and conventional ACO algorithm.In addition,simulation results obtained using an example of nonlinear system shows clearly the feasibility and the applicability of the proposed control method and the superiority of the GHSACO method compared to the HS,its variants,particle swarm optimization,and genetic algorithms applied to the same problem.Originality/value–The proposed new GHS algorithm is more efficient than the original HS method and its most known variants IHS and GHS.The proposed control method展开更多
针对电力负荷的特点,综合考虑了温度及日期类型等因素对日最大负荷的影响,提出了一种采用模糊神经网络进行短期负荷预测的方法,并详细介绍了该方法的实现过程。通过对EUNITE(the European Network of Excellence on Intelligent Technol...针对电力负荷的特点,综合考虑了温度及日期类型等因素对日最大负荷的影响,提出了一种采用模糊神经网络进行短期负荷预测的方法,并详细介绍了该方法的实现过程。通过对EUNITE(the European Network of Excellence on Intelligent Technologies for Smart Adaptive Systems)网络提供的实际数据进行详细分析确定了影响日最大负荷的相关因素,进而选择了合适的模糊输入以建立相应的模糊神经网络预测模型,并取得了较为理想的预测结果。算例分析结果充分证明了模糊神经网络在短期电力负荷预测方面具有较好的应用前景。展开更多
文摘Fault detection and diagnosis for pneumatic system of automatic productionline are studied. An expert system using fuzzy-neural network and pneumatic circuit fault diagnosisinstrument are deigned. The mathematical model of various pneumatic faults and experimental deviceare built. In the end, some experiments are done, which shows that the expert system usingfuzzy-neural network can diagnose fast and truly fault of pneumatic circuit.
文摘This paper presents an improved design for the hypersonic reentry vehicle(HRV) by the trajectory linearization control(TLC) technology for the design of HRV. The physics-based model fails to take into account the external disturbance in the flight envelope in which the stability and control derivatives prove to be nonlinear and time-varying, which is likely in turn to increase the difficulty in keeping the stability of the attitude control system. Therefore, it is of great significance to modulate the unsteady and nonlinear characteristic features of the system parameters so as to overcome the disadvantages of the conventional TLC technology that can only be valid and efficient in the cases when there may exist any minor uncertainties. It is just for this kind of necessity that we have developed a fuzzy-neural disturbance observer(FNDO) based on the B-spline to estimate such uncertainties and disturbances concerned by establishing a new dynamic system. The simulation results gained by using the aforementioned technology and the observer show that it is just due to the innovation of the adaptive trajectory linearization control(ATLC) system. Significant improvement has been realized in the performance and the robustness of the system in addition to its fault tolerance.
文摘Non-linearity and parameter time-variety are inherent properties of lateral motions of a vehicle. How to effectively control intelligent vehicle (IV) lateral motions is a challenging task. Controller design can be regarded as a process of searching optimal structure from controller structure space and searching optimal parameters from parameter space. Based on this view, an intelligent vehicle lateral motions controller was designed. The controller structure was constructed by T-S fuzzy-neural network (FNN). Its parameters were searched and selected with genetic algorithm (GA). The simulation results indicate that the controller designed has strong robustness, high precision and good ride quality, and it can effectively resolve IV lateral motion non-linearity and time-variant parameters problem.
文摘The paper describes results obtained in the development of adaptive fuzzy-neural navigation subsystem for mobile legged robot. In order to keep the motion sufficiently smooth, free of sharp turnings and transversal swings when moving between closely located obstacles, fuzzy rules are updated on-line. To this end, the fuzzy rules are expressed through a layered feed-forward neural network and parameters are updated on line in two steps--the rough and fine updating. That is followed by the description of the learning fault diagnosis using binary neural network based on the Carpenter and Grossbergs＇ adaptive resonance theory.
文摘Purpose–The two main purposes of this paper are:first,the development of a new optimization algorithm called GHSACO by incorporating the global-best harmony search(GHS)which is a stochastic optimization algorithm recently developed,with the ant colony optimization(ACO)algorithm.Second,design of a new indirect adaptive recurrent fuzzy-neural controller(IARFNNC)for uncertain nonlinear systems using the developed optimization method(GHSACO)and the concept of the supervisory controller.Design/methodology/approach–The novel optimization method introduces a novel improvization process,which is different from that of the GHS in the following aspects:a modified harmony memory representation and conception.The use of a global random switching mechanism to monitor the choice between the ACO and GHS.An additional memory consideration selection rule using the ACO random proportional transition rule with a pheromone trail update mechanism.The developed optimization method is applied for parametric optimization of all recurrent fuzzy neural networks adaptive controller parameters.In addition,in order to guarantee that the system states are confined to the safe region,a supervisory controller is incorporated into the IARFNNC global structure.Findings–First,to analyze the performance of GHSACO method and shows its effectiveness,some benchmark functions with different dimensions are used.Simulation results demonstrate that it can find significantly better solutions when compared with the Harmony Search(HS),GHS,improved HS(IHS)and conventional ACO algorithm.In addition,simulation results obtained using an example of nonlinear system shows clearly the feasibility and the applicability of the proposed control method and the superiority of the GHSACO method compared to the HS,its variants,particle swarm optimization,and genetic algorithms applied to the same problem.Originality/value–The proposed new GHS algorithm is more efficient than the original HS method and its most known variants IHS and GHS.The proposed control method
文摘针对电力负荷的特点,综合考虑了温度及日期类型等因素对日最大负荷的影响,提出了一种采用模糊神经网络进行短期负荷预测的方法,并详细介绍了该方法的实现过程。通过对EUNITE(the European Network of Excellence on Intelligent Technologies for Smart Adaptive Systems)网络提供的实际数据进行详细分析确定了影响日最大负荷的相关因素,进而选择了合适的模糊输入以建立相应的模糊神经网络预测模型,并取得了较为理想的预测结果。算例分析结果充分证明了模糊神经网络在短期电力负荷预测方面具有较好的应用前景。
