Structural simplification of FMLP

The number of fuzzy rules directly determines the complexity and efficiency of a fuzzy multilayer perceptron (FMLP). Based on the neural network self-configuring learning (NNSCL) algorithm, the NNSCL-I algorithm is obtained by using the generalized inverse matrix (GIM) algorithm to adjust the remaining weights after pruning neurons. The NNSCL-I algorithm is applied in the rule-reasoning layer of the FMLP to simplify its rules and structure with no degradation in the original performance. Experimental results show the effectiveness and the feasibility of the algorithm.