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Continuous process improvement through inductive and analogical learning
Author(s) -
Saraiva Pedro M.,
Stephanopoulos George
Publication year - 1992
Publication title -
aiche journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.958
H-Index - 167
eISSN - 1547-5905
pISSN - 0001-1541
DOI - 10.1002/aic.690380202
Subject(s) - computer science , process (computing) , quality (philosophy) , selection (genetic algorithm) , set (abstract data type) , fuzzy logic , class (philosophy) , artificial intelligence , product (mathematics) , machine learning , function (biology) , fuzzy set , rule induction , data mining , industrial engineering , engineering , mathematics , philosophy , geometry , epistemology , evolutionary biology , biology , programming language , operating system
Abstract This article presents a methodology for the continuous detection and definition of process performance improvement opportunities, especially as these pertain to the quality of operations (such as product quality). The problem is first reduced to an essentially pattern recognition formulation, for which an integrated and adaptive methodology combining analogical reasoning and symbolic induction is developed. The resulting classification of past records of data is used to support the construction of a decision support system for the generation/selection of operating suggestions leadin to performance improvements. The overall approach complements the usual set of statistical tools, commonly employed to address quality management problems. The basic methodology is also extended to handle fuzzy class definitions and function learning formulations. Case studies, covering both simulated and real industrial situations, illustrate the concepts and their practical utility.