Abstract
Gradient techniques and genetic algorithms are currently the most widely used parameters learning methods for fuzzy neural networks. Since Gradient techniques search for local solutions and GA is easy to premature, tabu search algorithms are currently being investigated for the development of adaptive or self-tuning neuro-fuzzy approximator(NFA). By using the globe search technique, the fuzzy inference rules are built automatically. To show the effectiveness of this methodology, it has been used for modeling static nonlinear systems.
This research was supported by key project of Ministry of Education, China (104262) and fund project of Chongqing Science and technology Commission (2003-7881).
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© 2004 Springer-Verlag Berlin Heidelberg
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Liu, G., Fang, Y., Zheng, X., Qiu, Y. (2004). Tuning Neuro-Fuzzy Function Approximator by Tabu Search. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks – ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3173. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28647-9_47
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DOI: https://doi.org/10.1007/978-3-540-28647-9_47
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