Designing fuzzy inference system based on improved gradient descent method

作     者：Zhang Liquan Shao Cheng 

作者机构：Research Center of Information and Control Dalian Univ. of Technology Dalian 116024 P. R. China 

基　　金：This project was supported by State Science &Technology Pursuing Project (2001BA204B01) of China and Foundation forUniversity Key Teacher by the Ministry of Education of China 

出 版 物：《Journal of Systems Engineering and Electronics》 (系统工程与电子技术（英文版）)

年 卷 期：2006年第17卷第4期

页      码：853-857,863页

摘      要：The distribution of sampling data influences completeness of rule base so that extrapolating missing rules is very difficult. Based on data mining, a self-learning method is developed for identifying fuzzy model and extrapolating missing rules, by means of confidence measure and the improved gradient descent method. The proposed approach can not only identify fuzzy model, update its parameters and determine optimal output fuzzy sets simultaneously, but also resolve the uncontrollable problem led by the regions that data do not cover. The simulation results show the effectiveness and accuracy of the proposed approach with the classical truck backer-upper control problem verifying.

主 题 词：data mining fuzzy system gradient descent method missing rule. 

学科分类：12[管理学] 1201[管理学-管理科学与工程类] 081104[081104] 08[工学] 0835[0835] 0811[工学-水利类] 0812[工学-测绘类] 

核心收录：

D　O　I：10.1016/S1004-4132(07)60027-9

馆 藏 号：203173115...