Title: A fast method for fuzzy neural network modelling and refinement

Authors: Barbara Pizzileo, Kang Li, George W. Irwin

Addresses: School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, Ashby Building, Belfast BT9 5AH, UK. ' School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, Ashby Building, Belfast BT9 5AH, UK. ' School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, Ashby Building, Belfast BT9 5AH, UK

Abstract: In the identification of complex dynamic systems using fuzzy neural networks, one of the main issues is the curse of dimensionality, which makes it difficult to retain a large number of system inputs or to consider a large number of fuzzy sets. Moreover, due to the correlations, not all possible network inputs or regression vectors in the network are necessary and adding them simply increases the model complexity and deteriorates the network generalisation performance. In this paper, the problem is solved by first proposing a fast algorithm for selection of network terms, and then introducing a refinement procedure to tackle the correlation issue. Simulation results show the efficacy of the method.

Keywords: fuzzy neural networks; FNNs; regressors selection; model refinement; fuzzy logic; dimensionality; fuzzy sets; network terms; correlation; simulation.

DOI: 10.1504/IJMIC.2009.029261

International Journal of Modelling, Identification and Control, 2009 Vol.8 No.3, pp.175 - 183

Available online: 17 Nov 2009

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