The proposed controller incorporates FL （fuzzy logic） algorithm with ANN （artificial neural network）. ANFIS replaces the conventional PI controller, tuning the fuzzy inference system with a hybrid learning algorit...The proposed controller incorporates FL （fuzzy logic） algorithm with ANN （artificial neural network）. ANFIS replaces the conventional PI controller, tuning the fuzzy inference system with a hybrid learning algorithm. A tuning method is proposed for training of the neuro-fuzzy controller. The best rule base and the best training algorithm chosen produced high performance in the ANFIS controller. Simulation was done on Matlab Ver. 2010a. A case study was chopper-fed DC motor drive, in continuous and discrete modes. Satisfactory results show the ANFIS controller is able to control dynamic highly-nonlinear systems. Tuning it further improved the results.展开更多
Based on the structure of Elman and Jordan neural networks, a new dynamic neural network is constructed. The network can remember the past state of the hidden layer and adjust the effect of the past signal to the curr...Based on the structure of Elman and Jordan neural networks, a new dynamic neural network is constructed. The network can remember the past state of the hidden layer and adjust the effect of the past signal to the current value in real-time. And in order to enhance the signal processing capabilities, the feedback of output layer nodes is increased. A hybrid learning algorithm based on genetic algorithm （GA） and error back propagation algorithm （BP） is used to adjust the weight values of the network, which can accelerate the rate of convergence and avoid getting into local optimum. Finally, the improved neural network is utilized to identify underwater vehicle （UV） ＇ s hydrodynamic model, and the simulation results show that the neural network based on hybrid learning algorithm can improve the learning rate of convergence and identification nrecision.展开更多
文摘The proposed controller incorporates FL （fuzzy logic） algorithm with ANN （artificial neural network）. ANFIS replaces the conventional PI controller, tuning the fuzzy inference system with a hybrid learning algorithm. A tuning method is proposed for training of the neuro-fuzzy controller. The best rule base and the best training algorithm chosen produced high performance in the ANFIS controller. Simulation was done on Matlab Ver. 2010a. A case study was chopper-fed DC motor drive, in continuous and discrete modes. Satisfactory results show the ANFIS controller is able to control dynamic highly-nonlinear systems. Tuning it further improved the results.
基金Supported by the Postdoctoral Science Foundation of China（ No. 20100480964 ） , the Basic Research Foundation of Central University （ No. HEUCF100104） and the National Natural Science Foundation of China （No. 50909025/E091002）.
文摘Based on the structure of Elman and Jordan neural networks, a new dynamic neural network is constructed. The network can remember the past state of the hidden layer and adjust the effect of the past signal to the current value in real-time. And in order to enhance the signal processing capabilities, the feedback of output layer nodes is increased. A hybrid learning algorithm based on genetic algorithm （GA） and error back propagation algorithm （BP） is used to adjust the weight values of the network, which can accelerate the rate of convergence and avoid getting into local optimum. Finally, the improved neural network is utilized to identify underwater vehicle （UV） ＇ s hydrodynamic model, and the simulation results show that the neural network based on hybrid learning algorithm can improve the learning rate of convergence and identification nrecision.
