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Possibility-based fuzzy neural networks and their application to image processing

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3 Author(s)
Li Chen ; Dept. of Comput. Sci., Utah State Univ., Logan, UT, USA ; D. H. Cooley ; Jianping Zhang

This paper describes the foundations for a class of fuzzy neural networks. Such a network is a composite or two-stage network consisting of a fuzzy network stage and a neural network stage. It exhibits the ability to classify complex feature set vectors with a configuration that is simpler than that needed by a standard neural network, Unlike a standard neural network, this network is able to accept as input a vector of scalar values, or a vector (set) of possibility functions. The first stage of the network is fuzzy based. It has two parts: a parameter computing network (PCN), followed by a converting layer. In the PCN the weights of the nodes are possibility functions, and hence, the output of this network is a fuzzy set. The second part of this stage, which is a single layer network, then converts this fuzzy set into a scalar vector for input to the second stage. The second stage of the network is a standard backpropagation based neural network. In addition to establishing the theoretical foundations for such a network, this paper presents sample applications of the network for classification problems in satellite image processing and seismic lithology pattern recognition

Published in:

IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)  (Volume:29 ,  Issue: 1 )