Fuzzy Genetic Algorithm Based Inductive Learning System (FGALS): A New Machine Learning Approach and Application For Chemical Process Fault Diagnosis
نویسندگان
چکیده
In today’s complex chemical processes, extracting of general knowledge from the noisy raw process data, coming continuously from the sensors, is an important issue. In this paper, an approach for symbolic knowledge extracting from noisy raw process data based on genetic algorithms (GAs), namely Fuzzy Genetic Algorithm based inductive Learning System (FGALS), is illustrated. The developed system is able to extract knowledge from the process data in the form of natural language like fuzzy rules. The system is also able to use available domain knowledge and it is robust to noise. The applicability of the developed system for fault diagnosis is shown on a hydrocarbon chlorination plant.
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تاریخ انتشار 2004