A Rule-Optimization Algorithm Based On Fuzzy Neural NetworkRule-Optimization Algorithm

摘要

A new network for fuzzy-neural system was proposed based on the- analysis and comparison of existing methods, which could be easy to distill the fuzzy rules. The network structure was adjusted by .FBP(Fuzzy Back Propagation) learning algorithm to acquire network parameters and variable weights. By aiming at disadvantage of IP algorithm on rule-optimization, the Improved Iterative Pruning Neural Network (IIP) algorithm could lessen the network structure and reduce the complexity of compute to speed up the respond rate of output. The simulation results by fertilizer knowledge model demonstrate the effectiveness and feasibility of proposed rule-optimization algorithm based on FNN.