Prediction and Identification Using Wavelet-Based Recurrent Fuzzy Neural Networks
IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)Published 20 September 2004
Chung‐Jung Lin, Chian-Song Chin
Citations152
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TL;DR
The proposed WRFNN model combines the traditional Takagi-Sugeno-Kang fuzzy model and the wavelet neural networks to create a wavelet-based recurrent fuzzy neural network for prediction and identification of nonlinear dynamic systems.
Abstract
This paper presents a wavelet-based recurrent fuzzy neural network (WRFNN) for prediction and identification of nonlinear dynamic systems. The proposed WRFNN model combines the traditional Takagi-Sugeno-Kang (TSK) fuzzy model and the wavelet neural networks (WNN). This paper adopts the nonorthogonal and compactly supported functions as wavelet neural network bases. Temporal relations embedded in the network are caused by adding some feedback connections representing the memory units into the second layer of the feedforward wavelet-based fuzzy neural networks (WFNN). An online learning algorithm, which consists of structure learning and parameter learning, is also presented. The structure learning depends on the degree measure to obtain the number of fuzzy rules and wavelet functions. Meanwhile, the parameter learning is based on the gradient descent method for adjusting the shape of the membership function and the connection weights of WNN. Finally, computer simulations have demonstrated that the proposed WRFNN model requires fewer adjustable parameters and obtains a smaller rms error than other methods.
Keywords
Computer ScienceEngineering
IEEE Transactions on Systems Man and CyberneticsFuzzy identification of systems and its applications to modeling and control
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IEEE Transactions on Neural NetworksIdentification and control of dynamical systems using neural networks
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