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New algorithm for self-organizing neural classifiers suitable for easy hardware implementation

Artificial neural networks (ANN), or connectionist classifiers, are massively parallel computation systems that are based on simplified models of the human brain. Their complex classifications capabilities, combined with properties such as generalization, fault-tolerance and learning make them attractive for a range of applications that conventional computers found difficult. One of the possible neural net applications is an analysis of high dimension data sets. Thanks to mentioned above classifications capabilities, net output signals are low-dimension representations of inputs where each output can represented some input signal feature. In this paper we present the new algorihtm of multivariate data classification. The algorithm based on modified counterpropagation neural network. The main goal of our research as to develop a new classifier architecture which reduces the required number of interconnection in a hidden layer as well as output layer. That allows easier hardware implementation of proposed algorithm.