A self-learning fuzzy system design and modeling approach based on TSK fuzzy model is proposed in this paper. On the basis of the input-output training data, the nonlinear (membership function) and linear (weighting c...
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ISBN:
(纸本)9781424417339
A self-learning fuzzy system design and modeling approach based on TSK fuzzy model is proposed in this paper. On the basis of the input-output training data, the nonlinear (membership function) and linear (weighting coefficient) parameters in the IF and THEN part of fuzzy rules were separately optimized by supervised Gaussian learning and steady state Kalman filter, respectively. Finally, the proposed approach was successfully applied to visual evoked potential (VEP) extraction.
This paper presents a hybrid learning algorithm for Fuzzy Wavelet Neural Network(FWNN) and uses it in nonlinear system *** algorithm gives the initial parameters by clustering algorithm,then updates them with a combin...
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This paper presents a hybrid learning algorithm for Fuzzy Wavelet Neural Network(FWNN) and uses it in nonlinear system *** algorithm gives the initial parameters by clustering algorithm,then updates them with a combination of Back-Propagation and Recursive Least Square methods. The proposed approach is tested for identification of nonlinear systems commonly used in the *** is shown that with the proposed approach the number of rules and complexity of the structure will be reduced while the performance is better than the previous *** order to comparison,Gradient Descent algorithm is applied in the same *** results indicate a superior convergence speed for the proposed algorithm in comparison to Gradient Descent method which is commonly used in the literature.
This paper considers an evolutionary extreme learning machine (ELM) based on chemical reaction optimization (CRO) to overcome the drawbacks of ELM, such as the unavoidable existence of a set of unnecessary or non-opti...
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This paper considers an evolutionary extreme learning machine (ELM) based on chemical reaction optimization (CRO) to overcome the drawbacks of ELM, such as the unavoidable existence of a set of unnecessary or non-optimal hidden biases and input weights. By using CRO algorithm to determine the hidden biases and input weights according to both the norm of output weights and the root mean squared error, the classification performance of optimized ELM can be improved. The experimental results on some real benchmark problems show that the proposed method can achieve higher classification accuracy than both other compared evolutionary ELMs and original ELM.
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