Possibility-function-based neural networks: case study of mathematical analysis

Li Chen; Donald H. Cooley; Jianping Zhang

doi:10.1117/12.243266

14 June 1996 Possibility-function-based neural networks: case study of mathematical analysis

Li Chen, Donald H. Cooley, Jianping Zhang

Proceedings Volume 2761, Applications of Fuzzy Logic Technology III; (1996) https://doi.org/10.1117/12.243266
Event: Aerospace/Defense Sensing and Controls, 1996, Orlando, FL, United States

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.

Citation Download Citation

Li Chen, Donald H. Cooley, and Jianping Zhang "Possibility-function-based neural networks: case study of mathematical analysis", Proc. SPIE 2761, Applications of Fuzzy Logic Technology III, (14 June 1996); https://doi.org/10.1117/12.243266

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