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Neurofuzzy Adaptive Modelling and Construction of Nonlinear Dynamical Processes

Neurofuzzy Adaptive Modelling and Construction of Nonlinear Dynamical Processes
Neurofuzzy Adaptive Modelling and Construction of Nonlinear Dynamical Processes
The identification of nonlinear dynamical processes has become an important task in many different areas of research. The formulation of such models is inherently a very difficult task. Neurofuzzy modelling has recently been proposed to help tackle this idetification problem, where neural networks and fuzzy logic are combined, providing fuzzy systems to which thorough mathematical analysis can be applied. Fundamental to system identification is the principle of parsimony, where the best model is the one with simplest acceptable structure. This coupled with the curse of dimensionality has lead to the development of efficient off-line parsimonious neurofuzzy construction algorithms. This chapter discusses a range of neurofuzzy algorithms that automatically construct parsimonious models. In this discussion different construction algorithms and alternative (non-lattice based) neurofuzzy models are addressed.
253--292
Bossley, K.M.
de1a2979-b9e9-481e-af09-0b4887f0f360
Brown, M.
52cf4f52-6839-4658-8cc5-ec51da626049
Harris, C.J.
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Irwin, G.R.
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Warwick, K.
61ec73ef-55ab-434c-9f6d-56f283b44f09
Hunt, K.J.
532666af-4460-4554-9088-46a7805d84ba
Bossley, K.M.
de1a2979-b9e9-481e-af09-0b4887f0f360
Brown, M.
52cf4f52-6839-4658-8cc5-ec51da626049
Harris, C.J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
Irwin, G.R.
3a2e6d1b-186e-4f86-8015-c5d7ccbf17d6
Warwick, K.
61ec73ef-55ab-434c-9f6d-56f283b44f09
Hunt, K.J.
532666af-4460-4554-9088-46a7805d84ba

Bossley, K.M., Brown, M. and Harris, C.J. (1995) Neurofuzzy Adaptive Modelling and Construction of Nonlinear Dynamical Processes. Irwin, G.R., Warwick, K. and Hunt, K.J. (eds.) Neural Network Applications in Control. 253--292 .

Record type: Conference or Workshop Item (Other)

Abstract

The identification of nonlinear dynamical processes has become an important task in many different areas of research. The formulation of such models is inherently a very difficult task. Neurofuzzy modelling has recently been proposed to help tackle this idetification problem, where neural networks and fuzzy logic are combined, providing fuzzy systems to which thorough mathematical analysis can be applied. Fundamental to system identification is the principle of parsimony, where the best model is the one with simplest acceptable structure. This coupled with the curse of dimensionality has lead to the development of efficient off-line parsimonious neurofuzzy construction algorithms. This chapter discusses a range of neurofuzzy algorithms that automatically construct parsimonious models. In this discussion different construction algorithms and alternative (non-lattice based) neurofuzzy models are addressed.

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Published date: 1995
Venue - Dates: Neural Network Applications in Control, 1995-01-01
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 250143
URI: http://eprints.soton.ac.uk/id/eprint/250143
PURE UUID: c303450d-60fc-40f7-8067-0d46647fa784

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Date deposited: 04 May 1999
Last modified: 29 Jan 2020 14:56

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