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 > Southampton Wireless Group
ePrint ID: 250114
Accepted Date and Publication Date:
Date Deposited: 04 May 1999
Last Modified: 31 Mar 2016 13:50
Further Information:Google Scholar

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