PARALLEL SELF-ORGANIZING FEATURE MAPS FOR UNSUPERVISED PATTERN-RECOGNITION

Neural network research has recently undergone a revival for use in pattern recognition applications.1 If a training set of data can be provided, the supervised types of networks, such as the Hopfield nets or perceptrons, can be used to recognize patterns.10.11.18 For unsupervised pattern recognition, systems such as those of the Carpenter/Grossberg ART2 system8 and Kohonens' self-organizing feature maps11 are the most commonly used. The problem of poor separability of input vectors was recently addressed by Keller and Hunt with the fuzzy perceptron model.13 However, with the exception of the ART2 system, none of these systems are capable of producing continuous valued output, as would be a desirable model for representation of non-distinct input vectors. This paper presents four new algorithms based on the Kohonen self-organizing feature maps which are capable of generating a continuous valued output.4 We also present the results of some experimental studies run on the NCUBE/10 hypercube at the University of South Carolina. © 1990, Taylor & Francis Group, LLC. All rights reserved.