摘要
针对模糊神经网络结构设计问题,提出一种基于递归聚类与相似性的结构设计方法.首先,提出以输出变化强度为导向、以结构细分为手段的递归聚类方法对网络初始结构进行设计.其次,通过计算模糊规则的相似性,将高度相似的规则进行合并,在保持良好精度的前提下,对网络初始结构进行简化.最后,通过函数逼近、非线性系统辨识仿真实验验证了方法的可行性和有效性.
Facing the structure design problem of fuzzy neural networks（ FNNs）,this paper proposed a structure design approach based on the recursive clustering and similarity methods. First,a recursive clustering method to identify FNN structure was proposed. Guided by the strength of output variations and using the recursive sub-clustering as the means,the proposed method determined the initial network structure through recursive iterations. Second,maintaining a high accuracy,the method calculated the similarity degree between each pair of fuzzy rules and then merged highly similar rules to simplify the initialized structure of the FNN. Finally,numerical experiments in function approximation and nonlinear system identification were used to verify the feasibility and effectiveness of the proposed approach.
出处
《北京工业大学学报》
CAS
CSCD
北大核心
2017年第2期210-216,共7页
Journal of Beijing University of Technology
基金
国家自然科学基金资助项目(61533002)
国家杰出青年科学基金资助项目(61225016)
北京市科技新星计划(Z131104000413007)
关键词
模糊神经网络
结构设计
递归聚类
相似性
fuzzy neural networks
structure design
recursive clustering
similarity
