Neurofuzzy Model Construction for the Modelling of Non-linear Processes
Bossley, K.M., Brown, M. and Harris, C.J. (1995) Neurofuzzy Model Construction for the Modelling of Non-linear Processes. 3rd European Control conference , 2438--2443.
Full text not available from this repository.
Neurofuzzy systems are ideal for modelling nonlinear processes; combining the transparent knowledge representation of fuzzy systems with the well established learning techniques of associative memory networks. In the control community there is a need for high-dimensional modelling, but unfortunately these neurofuzzy systems suffer from the curse of dimensionality. To make the use of high-dimensional neurofuzzy systems practical, off-line construction algorithms need to be developed which generate parsimonious models. This need for off-line construction algorithms is enhanced by the lack of expert knowledge from which models are traditionally developed. This paper outlines the concepts of B-spline neurofuzzy systems and presents several representations which can be exploited to produce parsimonious models. Finally, an off-line construction algorithm based on several of these representations is illustrated with a simple example.
|Item Type:||Conference or Workshop Item (UNSPECIFIED)|
|Additional Information:||Address: Rome, Italy|
|Divisions:||Faculty of Physical Sciences and Engineering > Electronics and Computer Science > Southampton Wireless Group
|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.|
Actions (login required)