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Title: Intelligent system for automatic feature detection and selection or identification

Abstract

A neural network uses a fuzzy membership function, the parameters of which are adaptive during the training process, to parameterize the interconnection weights between an (n{minus}1)`th layer and an n`th layer of the network. Each j`th node in each k`th layer of the network except the input layer produces its output value y{sub k,j} according to the function shown in Equation 1 where N{sub k{minus}1} is the number of nodes in layer k{minus}1, i indexes the nodes of layer k{minus}1 and all the w{sub k,i,j} are interconnection weights. The interconnection weights to all nodes j in the n`th layer are given by w{sub n,i,j}=w{sub n,j} (i, p{sub n,j,1}, . . . , p{sub n,j},p{sub n}). The apparatus is trained by setting values for at least one of the parameters p{sub n,j,1}, . . . , p{sub n,j},Pn. Preferably the number of parameters P{sub n} is less than the number of nodes N{sub n{minus}1} in layer n{minus}1. W{sub n,j} (i,p{sub n,j,1}, . . . , p{sub n,j},Pn) can be convex in i, and it can be bell-shaped. Sample functions for w{sub n,j} (i, p{sub n,j,1}, . . . , p{sub n,j},Pn) include Equation 2, shown in the patent. 8 figs.

Inventors:
; ; ;
Issue Date:
Research Org.:
University of California
OSTI Identifier:
527782
Patent Number(s):
5,664,066
Application Number:
PAN: 8-304,660
Assignee:
Dept. of Energy, Washington, DC (United States) PTO; SCA: 990200; PA: EDB-97:128886; SN: 97001846766
DOE Contract Number:  
W-7405-ENG-48
Resource Type:
Patent
Resource Relation:
Other Information: PBD: 2 Sep 1997
Country of Publication:
United States
Language:
English
Subject:
99 MATHEMATICS, COMPUTERS, INFORMATION SCIENCE, MANAGEMENT, LAW, MISCELLANEOUS; NEURAL NETWORKS; IDENTIFICATION SYSTEMS; FUZZY LOGIC; TRAINING; WEIGHTING FUNCTIONS

Citation Formats

Sun, C.T., Shiang, P.S., Jang, J.S., and Fu, C.Y. Intelligent system for automatic feature detection and selection or identification. United States: N. p., 1997. Web.
Sun, C.T., Shiang, P.S., Jang, J.S., & Fu, C.Y. Intelligent system for automatic feature detection and selection or identification. United States.
Sun, C.T., Shiang, P.S., Jang, J.S., and Fu, C.Y. Tue . "Intelligent system for automatic feature detection and selection or identification". United States.
@article{osti_527782,
title = {Intelligent system for automatic feature detection and selection or identification},
author = {Sun, C.T. and Shiang, P.S. and Jang, J.S. and Fu, C.Y.},
abstractNote = {A neural network uses a fuzzy membership function, the parameters of which are adaptive during the training process, to parameterize the interconnection weights between an (n{minus}1)`th layer and an n`th layer of the network. Each j`th node in each k`th layer of the network except the input layer produces its output value y{sub k,j} according to the function shown in Equation 1 where N{sub k{minus}1} is the number of nodes in layer k{minus}1, i indexes the nodes of layer k{minus}1 and all the w{sub k,i,j} are interconnection weights. The interconnection weights to all nodes j in the n`th layer are given by w{sub n,i,j}=w{sub n,j} (i, p{sub n,j,1}, . . . , p{sub n,j},p{sub n}). The apparatus is trained by setting values for at least one of the parameters p{sub n,j,1}, . . . , p{sub n,j},Pn. Preferably the number of parameters P{sub n} is less than the number of nodes N{sub n{minus}1} in layer n{minus}1. W{sub n,j} (i,p{sub n,j,1}, . . . , p{sub n,j},Pn) can be convex in i, and it can be bell-shaped. Sample functions for w{sub n,j} (i, p{sub n,j,1}, . . . , p{sub n,j},Pn) include Equation 2, shown in the patent. 8 figs.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {1997},
month = {9}
}