Optimization of hierarchical neural fuzzy models

Campello, Ricardo J.G.B., and Amaral, Wagner C. (2000) Optimization of hierarchical neural fuzzy models. In: Proceedings of the International Joint Conference on Neural Networks, pp. 8-13. From: IJCNN 2000: International Joint Conference on Neural Networks, 24-27 July 2000, Como, Italy.

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Abstract

Hierarchical fuzzy structures were introduced in previous work to deal with the dimensionality problem which is the main drawback to the application of neural networks and fuzzy models in the modeling and control of large-scale systems. In the present paper, the use of Radial Basis Function (RBF) networks connected in a hierarchical (cascade) fashion is investigated. The RBF networks are formulated as simplified fuzzy systems and the backpropagation equations for the optimization of the resulting hierarchical models are derived from this formulation. The optimization of the models using the conjugate gradient algorithm of Fletcher and Reeves is proposed and illustrated by means of a numerical example.

Item ID: 47626
Item Type: Conference Item (Research - E1)
ISBN: 978-0-7695-0619-7
Funders: FAPESP, CNPq
Projects and Grants: FAPESP (99/03902-6), CNPq (301345184)
Date Deposited: 08 Mar 2017 07:40
FoR Codes: 01 MATHEMATICAL SCIENCES > 0102 Applied Mathematics > 010299 Applied Mathematics not elsewhere classified @ 100%
SEO Codes: 97 EXPANDING KNOWLEDGE > 970101 Expanding Knowledge in the Mathematical Sciences @ 100%
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