title: Possibility-based fuzzy neural networks and their application to image processing.
abstract: 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.
authors.affiliation: Dept. of Comput. Sci., Utah State Univ., Logan, UT.
authors.forename: L
authors.initials: L
authors.lastname: Chen
authors.forename: D H
authors.initials: DH
authors.lastname: Cooley
authors.forename: J
authors.initials: J
authors.lastname: Zhang