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96657 COMPUTER LEARNING SYSTEMS IN CORROSION

Picture for 96657 COMPUTER LEARNING SYSTEMS IN CORROSION
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Product Number: 51300-96657-SG
ISBN: 96657 1996 CP
Author: C.P. Sturrock, W.F. Bogaerts
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A collection of data documenting the stress corrosion cracking (SCC) behavior of austenitic stainless steels provides the basis for an automated learning system. Computer learning systems based on classical and non-parametric statistics, connectionist models, machine learning methods, and fuzzy logic are described. An original method for inducing fuzzy rules from input-output data is presented. All of these computer learning systems are used to solve a typical problem of corrosion engineering: determine the likelihood of SCC of austenitic stainless steels given varying conditions of temperature, chloride level, oxygen content, and metallurgical condition in simulated boiling water reactor (BWR) environments. Empirical performance comparisons of the various approaches are summarized, along with the relative intelligibility of the outputs. In both areas the decision tree approach was found to perform very well on the problem investigated. Keywords: computer learning, decision tree, expert system, fuzzy logic, linear discriminant, nearest neighbor, polynomial network, stainless steel, stress corrosion cracking
A collection of data documenting the stress corrosion cracking (SCC) behavior of austenitic stainless steels provides the basis for an automated learning system. Computer learning systems based on classical and non-parametric statistics, connectionist models, machine learning methods, and fuzzy logic are described. An original method for inducing fuzzy rules from input-output data is presented. All of these computer learning systems are used to solve a typical problem of corrosion engineering: determine the likelihood of SCC of austenitic stainless steels given varying conditions of temperature, chloride level, oxygen content, and metallurgical condition in simulated boiling water reactor (BWR) environments. Empirical performance comparisons of the various approaches are summarized, along with the relative intelligibility of the outputs. In both areas the decision tree approach was found to perform very well on the problem investigated. Keywords: computer learning, decision tree, expert system, fuzzy logic, linear discriminant, nearest neighbor, polynomial network, stainless steel, stress corrosion cracking
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