Fuzzy Modeling Via On-Line Clustering and Support Vector Machine
This paper describes a novel fuzzy rule-based modeling approach for some slow industrial
processses. Structure identification is realized by clustering and support vector machines.
When the process is slow, fuzzy rules can be obtained automatically. Parameters
identification uses the techniques of fuzzy neural networks. A time-varying learning rate
assures stability of the modeling error.
processses. Structure identification is realized by clustering and support vector machines.
When the process is slow, fuzzy rules can be obtained automatically. Parameters
identification uses the techniques of fuzzy neural networks. A time-varying learning rate
assures stability of the modeling error.
Abstract
This paper describes a novel fuzzy rule-based modeling approach for some slow industrial processses. Structure identification is realized by clustering and support vector machines. When the process is slow, fuzzy rules can be obtained automatically. Parameters identification uses the techniques of fuzzy neural networks. A time-varying learning rate assures stability of the modeling error.
Springer
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