
Accession Number : ADD017551
Title : SelfOrganizing Neural Network for Classifying Pattern Signatures with a 'Posteriori' Conditional Class Probability.
Descriptive Note : Patent, Filed 28 Aug 92, patented 24 Jan 95,
Corporate Author : DEPARTMENT OF THE NAVY WASHINGTON DC
Personal Author(s) : Rogers, George W ; Solka, Jeffrey L ; Priebe, Carey E ; Poston, Wendy L
Report Date : 24 Jan 1995
Pagination or Media Count : 11
Abstract : A selforganizing neural network and method for classifying a pattern signature having Nfeatures is provided. The network provides a posteriori conditional class probability that the pattern signature belongs to a selected class from a plurality of classes with which the neural network was trained. In its training mode, a plurality of training vectors is processed to generate an Nfeature, Ndimensional space defined by a set of nonoverlapping trained clusters. Each training vector has Nfeature coordinates and a class coordinate. Each trained cluster has a center and a radius defined by a vigilance parameter. The center of each trained cluster is a reference vector that represents a recursive mean of the Nfeature coordinates from training vectors bounded by a corresponding trained cluster. Each reference vector defines a fractional probability associated with the selected class based upon a ratio of (1) a count of training vectors from the selected class that are bounded by the corresponding trained cluster to (2) a total count of training vectors bounded by the corresponding trained cluster. In the exercise mode, an input vector defines the pattern signature to he classified. The input vector has Nfeature coordinates associated with an unknown class. One of the reference vectors is selected so as to minimize differences with the Nfeature coordinates of the input vector. The fractional probability of the selected one of the reference vectors is the a posteriori conditional class probability that the input vector belongs to the selected class. (KAR) p. 1
Descriptors : *NEURAL NETS, *CLASSIFICATION, *PATTERN RECOGNITION, *PATENTS, *ADAPTIVE TRAINING, *SELF ORGANIZING SYSTEMS, RATIOS, PARAMETERS, VIGILANCE, PROBABILITY, COMPUTER ARCHITECTURE, CLUSTERING, COORDINATES, RECURSIVE FUNCTIONS, PATTERNS, SIGNATURES, MEAN, INFORMATION PROCESSING, NEUROBIOLOGY
Subject Categories : Cybernetics
Biology
Distribution Statement : APPROVED FOR PUBLIC RELEASE