Advances in Neurofuzzy Algorithms for Real-time Modelling and Control
Harris, C.J., Brown, M., Bossley, K.M., Mills, D.J. and Feng, M. (1996) Advances in Neurofuzzy Algorithms for Real-time Modelling and Control. J. Engineering Application of AI, 9, (1), 1--16.
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This paper reviews the architecture, representation capability, training and learning ability of a class of adaptive neurofuzzy systems for real time modelling and control of unknown nonlinear dynamical processes. Issues relating to learning stability, training laws and parametric convergence, network conditioning, gradient noise, the curse of dimensionality associated with associative memory networks, automatic network construction algorithms, and a series of neurofuzzy control design laws, are discussed together with future critical research issues associated with neurofuzzy systems.
|Divisions:||Faculty of Physical Sciences and Engineering > Electronics and Computer Science > Comms, Signal Processing & Control
|Date Deposited:||04 May 1999|
|Last Modified:||02 Mar 2012 13:39|
|Further Information:||Google Scholar|
|ISI Citation Count:||12|
|RDF:||RDF+N-Triples, RDF+N3, RDF+XML, Browse.|
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