Supervised and Reinforcement Evolutionary Learning for Wavelet-based Neuro-fuzzy Networks
| Autor: | Yong-Cheng Liu, Chi-Yung Lee, Cheng-Jian Lin |
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| Rok vydání: | 2008 |
| Předmět: |
Scheme (programming language)
Engineering Neuro-fuzzy business.industry Mechanical Engineering Evolutionary learning Machine learning computer.software_genre Industrial and Manufacturing Engineering Identification (information) Wavelet Artificial Intelligence Control and Systems Engineering Symbiotic evolution Reinforcement learning Artificial intelligence Electrical and Electronic Engineering business Reinforcement computer Software computer.programming_language |
| Zdroj: | Journal of Intelligent and Robotic Systems. 52:285-312 |
| ISSN: | 1573-0409 0921-0296 |
| DOI: | 10.1007/s10846-008-9214-9 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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