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Neurofuzzy design and model construction of nonlinear dynamical processes from data

Hong, X. and Harris, C. J. (2001) Neurofuzzy design and model construction of nonlinear dynamical processes from data. IEE Proceedings-Control Theory and Applications, 148 (6). pp. 530-538. ISSN 1350-2379

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To link to this article DOI: 10.1049/ip-cta:20010704


A common problem in many data based modelling algorithms such as associative memory networks is the problem of the curse of dimensionality. In this paper, a new two-stage neurofuzzy system design and construction algorithm (NeuDeC) for nonlinear dynamical processes is introduced to effectively tackle this problem. A new simple preprocessing method is initially derived and applied to reduce the rule base, followed by a fine model detection process based on the reduced rule set by using forward orthogonal least squares model structure detection. In both stages, new A-optimality experimental design-based criteria we used. In the preprocessing stage, a lower bound of the A-optimality design criterion is derived and applied as a subset selection metric, but in the later stage, the A-optimality design criterion is incorporated into a new composite cost function that minimises model prediction error as well as penalises the model parameter variance. The utilisation of NeuDeC leads to unbiased model parameters with low parameter variance and the additional benefit of a parsimonious model structure. Numerical examples are included to demonstrate the effectiveness of this new modelling approach for high dimensional inputs.

Item Type:Article
Divisions:Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:18501
Uncontrolled Keywords:A-optimality design , associative memory networks , data based modelling , fuzzy membership , fuzzy rule base , neural networks , neurofuzzy system design , nonlinear dynamical processes

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