Accession Number : ADD017551

Title :   Self-Organizing 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 self-organizing neural network and method for classifying a pattern signature having N-features 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 N-feature, N-dimensional space defined by a set of non-overlapping trained clusters. Each training vector has N-feature 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 N-feature 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 N-feature coordinates associated with an unknown class. One of the reference vectors is selected so as to minimize differences with the N-feature 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