Abstract
Using smooth membership functions and activation functions presumably enhances the performance of neural, fuzzy and neuro-fuzzy systems. In this work we present some results based on the efficient generation of gaussian piecewise-linear approximations and its application to neural/fuzzy parallel computing systems. The application of approximations to the gaussian nodes of radial basis function networks (RBFN), and the observation of the approximation capabilities of the networks after applying various learning algorithms, is revealing. We use the equivalence theorem between RBFN’s and certain fuzzy inference systems for extracting conclusions applicable to de fuzzy world.
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Basterretxea, K., Tarela, J.M., del Campo, I. (2004). Gaussian Function Approximation in Neuro-Fuzzy Systems. In: Lotfi, A., Garibaldi, J.M. (eds) Applications and Science in Soft Computing. Advances in Soft Computing, vol 24. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45240-9_23
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DOI: https://doi.org/10.1007/978-3-540-45240-9_23
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