Abstract
This paper describes how the fundamental principles of GAs can be hybridized with classical optimization techniques for the design of an evolutive algorithm for neuro-fuzzy systems. The proposed algorithm preserves the robustness and global search capabilities of GAs and improves on their performance, adding new capabilities to fine-tune the solutions obtained. © Springer-Verlag Berlin Heidelberg 2002.
Cite
CITATION STYLE
González, J., Rojas, I., Pomares, H., Prieto, A., & Goser, K. (2002). Evolutionary training of neuro-fuzzy patches for function approximation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2415 LNCS, pp. 559–564). Springer Verlag. https://doi.org/10.1007/3-540-46084-5_91
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