ICANN 2002: Artificial Neural Networks — ICANN 2002 pp 559-564 | Cite as
Evolutionary Training of Neuro-fuzzy Patches for Function Approximation
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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.
Keywords
Evolutive Algorithm Fuzzy System Input Space Cholesky Decomposition Triangular Membership Function
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© Springer-Verlag Berlin Heidelberg 2002