A fast method for fuzzy neural modeling and refinement

B. Pizzelio, Kang Li, George Irwin

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
Pages (from-to)175-183
Number of pages9
JournalInternational Journal Modelling, Identification and Control
Volume8 (3)
Publication statusPublished - 2009


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