A Modified Gradient-Based Neuro-Fuzzy Learning Algorithm for Pi-Sigma Network Based on First-Order Takagi-Sugeno System
- ISSN号:2095-2651
- 期刊名称:《数学研究及应用:英文版》
- 时间:0
- 分类:TP273.4[自动化与计算机技术—控制科学与工程;自动化与计算机技术—检测技术与自动化装置] TP18[自动化与计算机技术—控制科学与工程;自动化与计算机技术—控制理论与控制工程]
- 作者机构:[1]School of Mathematical Sciences, Dalian University of Technology, Liaoning 116024, P. R. China, [2]School of Information Science and Engineering, Dalian Polytechnic University, Liaoning 116034, P. R. China
- 相关基金:Supported by the Fundamental Research Funds for the Central Universities;; the National Natural Science Foundation of China (Grant No.11171367);; the Youth Foundation of Dalian Polytechnic University (Grant No.QNJJ201308)
关键词:
模糊推理系统, 学习算法, 神经模糊, 梯度, 一阶, 网络, 误差函数, 强收敛性, first-order Takagi-Sugeno inference system, Pi-Sigma network, convergence.
中文摘要:
This paper presents a Pi-Sigma network to identify first-order Tagaki-Sugeno(T-S)fuzzy inference system and proposes a simplified gradient-based neuro-fuzzy learning algorithm.A comprehensive study on the weak and strong convergence for the learning method is made,which indicates that the sequence of error function goes to a fixed value,and the gradient of the error function goes to zero,respectively.
英文摘要:
This paper presents a Pi-Sigma network to identify first-order Tagaki-Sugeno(T-S) fuzzy inference system and proposes a simplified gradient-based neuro-fuzzy learning algorithm.A comprehensive study on the weak and strong convergence for the learning method is made,which indicates that the sequence of error function goes to a fixed value,and the gradient of the error function goes to zero,respectively.