Abstract
This paper focuses on seeking an appropriate number of rules for a T-S inference system. A growing and pruning strategy in neural network is employed, which relates one fuzzy rule’s contribution to the modeling accuracy by a statistic criterion, such that fuzzy rules is added/removed, whereas all the parameters can learn using EKF, both absolutely on-line and with small computation. A simulation for nonlinear system identification illustrates the good performance.
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Liao, L., Li, S. (2007). On-Line T-S Fuzzy Model Identification with Growing and Pruning Rules. In: Liu, D., Fei, S., Hou, ZG., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4491. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72383-7_60
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DOI: https://doi.org/10.1007/978-3-540-72383-7_60
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72382-0
Online ISBN: 978-3-540-72383-7
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