Abstract
Existing forms of adaptive resonance theory, e.g., ART2 and Fuzzy ART, employ similarity-based vigilance measures and contrast enhancement that is analog in nature. They use the “fast” or “fast-commit-slow-recode” learning rules, which do not guarantee convergence of clustering results. Therefore, they are not suitable for process sensor pattern monitoring which required geometrically based classifications. A modified version of the adaptive resonance theory, DART, was developed. DART uses a distance-based vigilance measure, a contrast enhancement procedure that is around the center of the prototype instead of around the null input, and the Kohonen learning rule to ensure convergence when accepting inputs that are highly correlated dynamically. The necessities of such modifications were demonstrated using a simple mathematical example: the Leonard−Kramer problem. The ability of DART to isolate different faults from operation history and to monitor operation in an adaptive manner for a complex plant is de...
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