Abstract
In this paper the authors present a novel classification of the diverse architectures for intelligent systems. The authors discuss the classification with particular reference to the current uninhabited areas that offer potential for future research. The authors then describe how their work on neurofuzzy systems fits into this model. The paper provides an overview of the proposed neurofuzzy architecture for approximate fuzzy reasoning. The term approximate fuzzy reasoning is employed to highlight an approximation to the conventional fuzzy reasoning approach which considerably simplifies the resulting architecture. The performance of the approach is demonstrated by its application to benchmark problems. Simulation results are presented using the Matlab neural network toolbox and these are compared with traditional neural networks; other fuzzy neural networks and conventional fuzzy reasoning approaches. The work demonstrates the advantage of a neurofuzzy approach and highlights the advantages of this architecture for a hardware realisation.
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Maguire, L.P., McGinnity, T.M., Glackin, B.P. (2002). Hybrid Intelligent Architectures using a Neurofuzzy Approach. In: Zimmermann, HJ., Tselentis, G., van Someren, M., Dounias, G. (eds) Advances in Computational Intelligence and Learning. International Series in Intelligent Technologies, vol 18. Springer, Dordrecht. https://doi.org/10.1007/978-94-010-0324-7_10
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DOI: https://doi.org/10.1007/978-94-010-0324-7_10
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