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Authors: Wanqing Zhao 1 ; Kang Li 1 ; George W. Irwin 1 and Qun Niu 2

Affiliations: 1 Queen's University Belfast, United Kingdom ; 2 Shanghai University, China

ISBN: 978-989-8425-83-6

Keyword(s): Fuzzy neural systems, Interpretable model, Differential evolution, Weighted fast recursive algorithm, ANFIS.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Computational Intelligence ; Fuzzy Systems ; Fuzzy Systems Design, Modeling and Control ; Learning and Adaptive Fuzzy Systems ; Neuro-Fuzzy Systems ; Soft Computing ; Soft Computing and Intelligent Agents ; System Identification and Fault Detection

Abstract: Many learning methods have been proposed for Takagi-Sugeno-Kang fuzzy neural modelling. However, despite achieving good global performance, the local models obtained often exhibit eccentric behaviour which is hard to interpret. The problem here is to find a set of input space partitions and, hence, to identify the corresponding local models which can be easily understood in terms of system behaviour. A new hybrid approach for the construction of a locally optimized, functional-link-based fuzzy neural model is proposed in this paper. Unlike the usual linear polynomial models used for the rule consequent, the functional link artificial neural network (FLANN) is employed here to achieve a nonlinear mapping from the original model input space. Our hybrid learning method employs a modified differential evolution method to give the best fuzzy partitions along with the weighted fast recursive algorithm for the identification of each local FLANN. Results from a motorcycle crash dataset are in cluded to illustrate the interpretability of the resultant model structure and the efficiency of the new learning technique. (More)

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Paper citation in several formats:
Zhao, W.; Li, K.; W. Irwin, G. and Niu, Q. (2011). A HYBRID APPROACH TO LOCALLY OPTIMIZED INTERPRETABLE PARTITIONS OF FUZZY NEURAL MODELS.In Proceedings of the International Conference on Evolutionary Computation Theory and Applications - Volume 1: FCTA, (IJCCI 2011) ISBN 978-989-8425-83-6, pages 461-465. DOI: 10.5220/0003626304610465

@conference{fcta11,
author={Wanqing Zhao. and Kang Li. and George W. Irwin. and Qun Niu.},
title={A HYBRID APPROACH TO LOCALLY OPTIMIZED INTERPRETABLE PARTITIONS OF FUZZY NEURAL MODELS},
booktitle={Proceedings of the International Conference on Evolutionary Computation Theory and Applications - Volume 1: FCTA, (IJCCI 2011)},
year={2011},
pages={461-465},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003626304610465},
isbn={978-989-8425-83-6},
}

TY - CONF

JO - Proceedings of the International Conference on Evolutionary Computation Theory and Applications - Volume 1: FCTA, (IJCCI 2011)
TI - A HYBRID APPROACH TO LOCALLY OPTIMIZED INTERPRETABLE PARTITIONS OF FUZZY NEURAL MODELS
SN - 978-989-8425-83-6
AU - Zhao, W.
AU - Li, K.
AU - W. Irwin, G.
AU - Niu, Q.
PY - 2011
SP - 461
EP - 465
DO - 10.5220/0003626304610465

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