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.
|Title of host publication||Lecture Notes in Computer Science |
|Number of pages||8|
|Publication status||Published - 2010|
|Name||Advanced Intelligent Computing Theories and Applications|