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Supervised and Reinforcement Evolutionary Learning for Wavelet-based Neuro-fuzzy Networks

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Abstract

This study presents a wavelet-based neuro-fuzzy network (WNFN). The proposed WNFN model combines the traditional Takagi–Sugeno–Kang (TSK) fuzzy model and the wavelet neural networks (WNN). This study adopts the non-orthogonal and compactly supported functions as wavelet neural network bases. A novel supervised evolutionary learning, called WNFN-S, is proposed to tune the adjustable parameters of the WNFN model. The proposed WNFN-S learning scheme is based on dynamic symbiotic evolution (DSE). The proposed DSE uses the sequential-search-based dynamic evolutionary (SSDE) method. In some real-world applications, exact training data may be expensive or even impossible to obtain. To solve this problem, the reinforcement evolutionary learning, called WNFN-R, is proposed. Computer simulations have been conducted to illustrate the performance and applicability of the proposed WNFN-S and WNFN-R learning algorithms.

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Authors and Affiliations

  1. Department of Electrical Engineering, National University of Kaohsiung, Kaohsiung City, Taiwan, 811, Republic of China

    Cheng-Jian Lin

  2. Department of Computer Science and Information Engineering, Chaoyang University of Technology, Wufong, Taichung County, Taiwan, 413, Republic of China

    Cheng-Jian Lin & Yong-Cheng Liu

  3. Department of Computer Science and Information Engineering, Nankai Institute of Technology, Nantou, Taiwan, 542, Republic of China

    Chi-Yung Lee

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  1. Cheng-Jian Lin
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  2. Yong-Cheng Liu
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Correspondence to Cheng-Jian Lin.

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Lin, CJ., Liu, YC. & Lee, CY. Supervised and Reinforcement Evolutionary Learning for Wavelet-based Neuro-fuzzy Networks. J Intell Robot Syst 52, 285–312 (2008). https://doi.org/10.1007/s10846-008-9214-9

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  • DOI: https://doi.org/10.1007/s10846-008-9214-9

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