Dynamic Fuzzy Neural Network Algorithm Based on Parameter Adjusting
MA Zi-long · Jisuanji gongcheng · 2010
The union of fuzzy logic and the neural network forms the fuzzy neural network, it simultaneously has the advantages of expressing the human knowledge, storing and learning distribution information storage. This paper proposes a Dynamic Fuzzy-Neural Network(D-FNN) algorithm based on parameter adjusting. It uses Extended Kalman Filter(EKF) method to divide the overall algorithm into the linearity and the misalignment part. The linear parameter is decided by Least Squares(LS) method and the filter method, the misalignment parameter is directly decided by the training sample and heuristic method, the linearity and the misalignment parameter can carry on the real-time renewal. Simulation results indicate that this algorithm can guarantee more succinct structure and shorter learning time.