Neurofuzzy Modelling Thesis Available.

[Kevin Bossley]

The following PhD. thesis is now available!

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     "Neurofuzzy Modelling Approaches in System Identification"
	   	          	 by Kevin Bossley
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This can be obtained from:
	   http://www.isis.ecs.soton.ac.uk/pub/theses/kmb:97/thesis.html

and for immediate information I have included the abstract below.


Abstract

  System identification is the task of constructing representative 
  models of processes and has become an invaluable tool in many 
  different areas of science and engineering.  Due to the inherent 
  complexity of many real world systems the application of    
  traditional techniques is limited.  In such instances more 
  sophisticated (so called intelligent) modelling approaches are 
  required.  Neurofuzzy modelling is one such technique, which by 
  integrating the attributes of fuzzy systems and neural networks 
  is ideally suited to system identification.  This attractive 
  paradigm combines the well established learning techniques of a 
  particular form of neural network i.e.\ generalised linear models 
  with  the transparent knowledge representation of fuzzy systems, 
  thus producing models which possess the ability to learn from 
  real world observations and whose behaviour can be described 
  naturally as a series of linguistic humanly understandable rules.  
  Unfortunately,  the application of these systems is limited to 
  low dimensional problems for which good quality expert knowledge 
  and data are available.   

  The work described in this thesis addresses this fundamental 
  problem with neurofuzzy modelling, as a result algorithms which
  are less sensitive to the quality of the {\em a priori} knowledge 
  and empirical data are developed.  The true modelling 
  capabilities of any  strategy is heavily reliant on the model's 
  structure, and hence an important (arguably the most important) 
  task is structure identification.  Also, due to the {\em curse of 
  dimensionality}, in high dimensional problems the size of 
  conventional neurofuzzy models gets prohibitively large.   These 
  issues are tackled by the development of automatic neurofuzzy 
  model identification algorithms, which exploit the available 
  expert knowledge and empirical data.  To alleviate problems 
  associated with the curse of dimensionality, aid model 
  generalisation and enhance model transparency, parsimonious 
  models are identified.  This is achieved by the application of 
  additive and multiplicative neurofuzzy models which exploit 
  structural redundancies found in conventional systems.

  The developed construction algorithms successfully identify 
  parsimonious models, but as a result of noisy and poorly 
  distributed empirical data, these models can still generalise 
  inadequately.  This problem is addressed by the application of 
  Bayesian inferencing techniques; a form of regularisation.
  Smooth model  outputs are assumed and superfluous model 
  parameters are controlled, sufficiently aiding model 
  generalisation and transparency, and data interpolation and 
  extrapolation.  By exploiting the structural decomposition of the 
  identified neurofuzzy models, an efficient local method of 
  regularisation is developed.                   

  All the methods introduced  in this thesis are illustrated on 
  many different examples, including simulated time series, complex
  functional  equations, and multi-dimensional dynamical systems.
  For many of these problems conventional neurofuzzy modelling is
  unsuitable, and the developed techniques have extended the range 
  of problems to which neurofuzzy modelling can successfully be 
  applied.        


Best Regards

Kevin Bossley

---
Real Time Systems Group
Parallel Applications Centre
2 Venture Road
Chilworth
Southampton SO16 7NP
http://www.pac.soton.ac.uk/
Tel : +44 (0) 1703 760834
Fax : +44 (0) 1703 760833