Self-Organising Intuitionistic Fuzzy Neural Networks Based on UKF
Xie Wen-biao · Dianzi xuebao · 2010
Because fuzzy sets exist deficiency on semantic description,much of the current research interest in neuro-fuzzy hybrid systems is focused on how to extend fuzzy neural networks.To deal with this problem,a self-organizing intuitionistic fuzzy networks based on UKF is presented.Firstly,structure of intuitionistic fuzzy networks and meanings of each layer is proposed.Sec-ondly,training algorithm is deduced,and LLS and UKF are used to learn linear and non-linear parameters respectively.Thirdly,guideline of how to generate a new rule is given,and method of error descending rate is used as fuzzy rule pruning strategy,so that rule which plays an unimportant role in the system is deleted.At last,typical experiments of function approximation,system identifi-cation and prediction of time-series indicate that a fuzzy network obtained by the proposed algorithm has a more tighten structure and better generalization than other algorithms.