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A Self-Evaluating Expert System for X-Ray Rocking Curve Analysis

Partridge T & Tjahjadi T

Int Journal of Intelligent Systems, vol 9, 1994, 493-517


X-ray rocking curve analysis is widely used in research and industry to investigate the perfection of a variety of natural and synthetic crystals. In this article a method is demonstrated for the effective self-evaluation of an expert system for X-ray rocking curve analysis. The method uses a combination of fuzzy logic and machine learning, the latter defined as a self-adaptive system that improves system performance over time. The heuristics of several experts are combined using rules, frames, and connection matrices. Each expert is weighted on the basis of experience and these credibility weights are used to influence the decision processes of the expert system. All weights are evaluated over time and the basis for evaluation is successful or unsuccessful expert system decisions. Individual rules are also evaluated and whenever a rule is shown to be ineffective it is hidden from the reasoning processes of the expert system. When new situations occur that have not been allowed for in the rules of the expert system, then existing rules are fine-tuned and changed to deal with these new facts. New rules are inferred and evaluated in the same way as the heuristics of the human experts.