中山大学学报（自然科学版）Issue(5)：20-24,5.
基于非线性动态系统辨识的 D-FNN算法研究
Research Based on D-FNN Algorithm on the Nonlinear Dynamic System Identification
摘要
Abstract
Dynamic Fuzzy Neural Network (D-FNN),which basic idea is to construct a RBF neural net-work based on extension,could be seen as a TSK fuzzy system,as well as a Gaussian RBF neural net-work based on normalized.Within D-FNN algorithms,not only parameters could be adjusted in the learn-ing process,but also the structure of fuzzy neural network could be automatically determined.Nonlinear parameters are directly decided by the training samples and Gaussian width,which only need one step training to achieve this goal.Due to the application of pruning strategies,network structure would not continue to grow,thus ensuring the generalization capability of the system.Simulations are performed on nonlinear dynamic system identification by using D-FNN,and the effectiveness and efficiency of D-FNN algorithm are proved by comparison with related algorithms.关键词
动态模糊神经网络/模糊规则/系统辨识/RBFKey words
D-FNN/fuzzy rule/system identification/Radial Basis Function分类
计算机与自动化引用本文复制引用
杨文茵,张德丰,王传胜..基于非线性动态系统辨识的 D-FNN算法研究[J].中山大学学报（自然科学版）,2014,(5):20-24,5.基金项目
广东省自然科学基金资助项目 （）
