Fuzzy polynomial neurons as neurofuzzy processing units |
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| Authors: | Byoung-Jun Park Witold Pedrycz Sung-Kwun Oh |
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| Affiliation: | (1) Department of Electrical & Computer Engineering, University of Alberta, Edmonton, T6R 2G7, Canada;(2) Systems Science Institute, Polish Academy of Sciences, Warsaw, Poland;(3) Department of Electrical Engineering, The University of Suwon, San 2-2, Wau-ri, Bongdam-eup, Hwaseong-si, Gyeonggi-do, 445-743, South Korea |
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| Abstract: | In this study, we introduce and study fuzzy polynomial neurons (FPNs) being regarded as generic processing units in neurofuzzy computing. The underlying topology of FPNs is formed through fuzzy rules, fuzzy inference and polynomials. Each polynomial offers a nonlinear mapping and is centred around a modal value of the corresponding membership functions defined in the input space of the neuron. The adjustable order of the polynomial is essential when addressing the level of nonlinearity to be handled in the approximation problem. We demonstrate that fuzzy polynomial neurons form a certain class of functional neurons and afterwards discuss their properties and an overall design process. Furthermore, these neurons are discussed in the context of universal approximation and universal approximators |
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| Keywords: | Fuzzy polynomial neuron Functional neuron Universal approximator Neurofuzzy computing architecture |
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