基于UKF的自适应模糊推理神经网络

如何生成最优的模糊规则数及模糊规则的自动生成和修剪是模糊神经网络训练算法研究的重点。针对这一问题,本文提出了基于UKF的自适应模糊推理神经网络(UKF-ANFIS)。首先,通过减法聚类确定UKF-ANFIS的模糊规则及其高斯隶属函数的中心和宽度参数;其次,分析了模糊神经网络的非线性动力系统表示,并用LLS和UKF分别学习线性和非线性的参数;然后,用误差下降率方法作为模糊规则修剪的策略,删除作用不大的规则;最后,通过典型的函数逼近和系统辨识实例,表明本文算法得到的模糊神经网络的结构更为紧凑,泛化性能也更佳。

An Adaptive Network Based Fuzzy Inference System Based on UKF

Much of the current research interest in neuro-fuzzy hybrid systems is focused on how to generate an optimal number of fuzzy rules in a neuro-fuzzy system and investigate the automated methods of adding and pruning fuzzy rules.To deal with this problem,an adaptive network based fuzzy inference system(ANFIS) based on UKF is presented.Firstly,fuzzy rules and their parameters of ANFIS-RR are obtained by subtractive clustering.Secondly,the parameters are learned by linear least square and the back propagation algorithm.Thirdly,the non-linear dynamical system expression of fuzzy networks is analyzed,and LLS and UKF are used to learn linear and non-linear parameters respectively.Then,a method of error descending rate is used as the fuzzy rule pruning strategy,so that the rule which plays an unimportant role in the system is deleted.Finally,by typical experiments of function approximation and system identification indicate that fuzzy networks obtained by the proposed algorithm has a more tightened structure and better generalization than other algorithms.