Advanced self-organizing polynomial neural network |
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Authors: | Dongwon Kim Gwi-Tae Park |
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Affiliation: | (1) Department of Electrical Engineering, Korea University, 1, 5-ka, Anam-dong, Seongbuk-ku, Seoul, 136-701, South Korea |
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Abstract: | In this paper, we introduce a concept of advanced self-organizing polynomial neural network (Adv_SOPNN). The SOPNN is a flexible
neural architecture whose structure is developed through a modeling process. But the SOPNN has a fatal drawback; it cannot
be constructed for nonlinear systems with few input variables. To relax this limitation of the conventional SOPNN, we combine
a fuzzy system and neural networks with the SOPNN. Input variables are partitioned into several subspaces by the fuzzy system
or neural network, and these subspaces are utilized as new input variables to the SOPNN architecture. Two types of the advanced
SOPNN are obtained by combining not only the fuzzy rules of a fuzzy system with SOPNN but also the nodes in a hidden layer
of neural networks with SOPNN into one methodology. The proposed method is applied to the nonlinear system with two inputs,
which cannot be identified by conventional SOPNN to show the performance of the advanced SOPNN. The results show that the
proposed method is efficient for systems with limited data set and a few input variables and much more accurate than other
modeling methods with respect to identification error. |
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Keywords: | Advanced self-organizing polynomial neural networks Small number of input variables Fuzzy system Neural networks Nonlinear system Identification Limited data set |
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