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Proceedings Paper

Possibility-function-based neural networks: case study of mathematical analysis
Author(s): Li Chen; Donald H. Cooley; Jianping Zhang
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Paper Abstract

In this paper, we give a theoretical analysis for a generalized fuzzy neural network created in our previous papers. This analysis includes a mathematical proof of the training formulas used by such a network. the fuzzy neural network can accept a set of possibility functions as input as well as a vector of scalar values. This network consists of three components: a parameter-computing network, a converting layer, and a standard backpropagation-based neural network. The output vector of each layer of the parameter-computing network is a possibility vector, each element of which is a possibility function. The output vector of the converting layer is a fuzzy set, which represents the class membership values. In this paper only the first two components are considered.

Paper Details

Date Published: 14 June 1996
PDF: 14 pages
Proc. SPIE 2761, Applications of Fuzzy Logic Technology III, (14 June 1996); doi: 10.1117/12.243266
Show Author Affiliations
Li Chen, Utah State Univ. (United States)
Donald H. Cooley, Utah State Univ. (United States)
Jianping Zhang, Utah State Univ. (United States)


Published in SPIE Proceedings Vol. 2761:
Applications of Fuzzy Logic Technology III
Bruno Bosacchi; James C. Bezdek, Editor(s)

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