Abstract
In this study, we introduce and investigate a class of neural architectures of self-organizing neural networks (SONN) that is based on a genetically optimized multilayer perceptron with polynomial neurons (PNs) or fuzzy polynomial neurons (FPNs), develop a comprehensive design methodology involving mechanisms of genetic optimization and carry out a series of numeric experiments. We distinguish between two kinds of SONN architectures, that is, (a) Polynomial Neuron (PN) based and (b) Fuzzy Polynomial Neuron (FPN) based self-organizing neural networks. The GA-based design procedure being applied at each layer of SONN leads to the selection of preferred nodes with specific local characteristics (such as the number of input variables, the order of the polynomial, and a collection of the specific subset of input variables) available within the network.
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© 2004 Springer-Verlag Berlin Heidelberg
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Park, HS., Oh, SK., Pedrycz, W., Kim, HK. (2004). Genetically Optimized Self-Organizing Neural Networks Based on PNs and FPNs. 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_27
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DOI: https://doi.org/10.1007/978-3-540-28647-9_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22841-7
Online ISBN: 978-3-540-28647-9
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