Abstract
The number of fuzzy rules directly determines the complexity and efficiency of Fuzzy Neural Network (FNN). The Iterative Pruning (IP) algorithm belongs to the Pruning Method, and it spends much time computing adjusting factors of the remaining weights. So the Improved Iterative Pruning (IIP) algorithm is put forward, which adopts dividing blocks strategy and uses the Generalized Inverse Matrix (GIM) algorithm to replace the Conjugate Gradient Precondition Normal Equation (CGPCNE) algorithm for updating the remaining weights. The IIP algorithm is applied in the rule-reasoning layer of FNN to simplify its rules and structure in a great extent and preserve a good level of accuracy and generalization ability without retraining after pruning. The simulation results demonstrate the effectiveness and the feasibility of the algorithm.
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