Previous [1] [2] [3] [4] [5] [6] [7] [8] [9] [10]

Journal of Inforamtion Science and Engineering, Vol.18 No.2, pp.187-210 (March 2002)

Inconsistency Resolution and Rule Insertion for Fuzzy
Rule-Based Systems

Hahn-Ming Lee, Jyh-Ming Chena nd Chun-Lin Liu
Department of Electronic Engineering
National Taiwan University of Science and Technology
Taipei, 106 Taiwan
E-mail: hmlee@et.ntust.edu.tw

In this paper fuzzy rule inconsistency resolution and fuzzy rule insertion methods are proposed for fuzzy neural networks. Necessity support and possibility support (referred to as support pair) are applied to detect and remove inconsistencies. In addition to the support pair, the concept of initial learning point is used to handle rule insertion. We demonstrate the use of the proposed methods in an example called the Knowledge Base Evaluator (KBE). After inconsistency resolution operations, learning is improved. Moreover, a new fuzzy rule is generated by setting initial learning point based on deleted conflict rule. The result of using rule insertion is much better than with inconsistency resolution alone.

Keywords: inconsistency resolution, necessity support, possibility support, rule insertion, fuzzy rule-base systems

Full Text () Retrieve PDF document (200203_03.pdf)

Received November 17, 2000; revised February 7, 2001; accepted March 16, 2001.
Communicated by Cheun-Tsai Sun.