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. © Springer-Verlag 2004.
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CITATION STYLE
Liu, G., Fang, Y., Zheng, X., & Qiu, Y. (2004). Tuning neuro-fuzzy function approximator by tabu search. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3173, 276–281. https://doi.org/10.1007/978-3-540-28647-9_47
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