標題: A FUZZY NEURAL-NETWORK FOR RULE ACQUIRING ON FUZZY CONTROL-SYSTEMS
作者: SHANN, JJ
FU, HC
交大名義發表
資訊工程學系
National Chiao Tung University
Department of Computer Science
關鍵字: FUZZY LOGIC CONTROL;NEURAL NETWORKS;LEARNING ALGORITHMS
公開日期: 12-May-1995
摘要: This paper presents a layer-structured fuzzy neural network (FNN) for learning rules of fuzzy-logic control systems. Initially, FNN is constructed to contain all the possible fuzzy rules. We propose a two-phase learning procedure for this network. The first phase is a error-backprop (EBP) training, and the second phase is a rule pruning. Since some functions of the nodes in the FNN have the competitive characteristics, the EBP training will converge quickly. After the training, a pruning process is performed to delete redundant rules for obtaining a concise fuzzy rule base. Simulation results show that for the truck backer-upper control problem, the training phase learns the knowledge of fuzzy rules in several dozen epochs with an error rate of less than 1%. Moreover, the fuzzy rule base generated by the pruning process contains only 14% of the initial fuzzy rules and is identical to the target fuzzy rule base.
URI: http://hdl.handle.net/11536/1922
ISSN: 0165-0114
期刊: FUZZY SETS AND SYSTEMS
Volume: 71
Issue: 3
起始頁: 345
結束頁: 357
Appears in Collections:Conferences Paper


Files in This Item:

  1. A1995RA04500008.pdf

If it is a zip file, please download the file and unzip it, then open index.html in a browser to view the full text content.