Neurofuzzy Algorithms for Model Identification: Structure and Parameter Determination
Brown, M., Bossley, K.M. and Harris, C.J. (1996) Neurofuzzy Algorithms for Model Identification: Structure and Parameter Determination. Computational Engineering in Systems Applications '96: Symposium on Control, Optimization and Supervision , 1061--1066.
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This paper describes some of the issues associated with developing a class of neurofuzzy construction algorithms based on B-spline fuzzy membership functions. These techniques have many desirable properties and links can be made with more conventional statistical model building approaches. The neurofuzzy model is decomposed into its linear and nonlinear components and a search technique is used to identify the structural nonlinearities whereas standard linear optimisation algorithms are used to identify the linear parameters. This paper discusses the performance of these two elements and contrasts their roles in the context of neurofuzzy systems.
|Item Type:||Conference or Workshop Item (UNSPECIFIED)|
|Additional Information:||Organisation: IMACS Address: Lille, France|
|Divisions:||Faculty of Physical Sciences and Engineering > Electronics and Computer Science > Comms, Signal Processing & Control
|Date Deposited:||04 May 1999|
|Last Modified:||27 Mar 2014 19:51|
|Further Information:||Google Scholar|
|RDF:||RDF+N-Triples, RDF+N3, RDF+XML, Browse.|
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