Abstract
To construct a compact fuzzy neural model with an appropriate number of inputs and rules is still a challenging problem. To reduce the number of basis vectors most existing methods select significant terms from the rule consequents, regardless of the structure and parameters in the premise. In this paper, a new integrated method for structure selection and parameter learning algorithm is proposed. The selection takes into account both the premise and consequent structures, thereby achieving simultaneously a more effective reduction in local model inputs relating to each rule, the total number of fuzzy rules, and the whole network inputs. Simulation results are presented which confirm the efficacy and superiority of the proposed method over some existing approaches.
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Zhao, W., Li, K., Irwin, G.W., Fei, M. (2010). An Integrated Method for the Construction of Compact Fuzzy Neural Models. In: Huang, DS., Zhao, Z., Bevilacqua, V., Figueroa, J.C. (eds) Advanced Intelligent Computing Theories and Applications. ICIC 2010. Lecture Notes in Computer Science, vol 6215. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14922-1_14
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DOI: https://doi.org/10.1007/978-3-642-14922-1_14
Publisher Name: Springer, Berlin, Heidelberg
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