Bidirectional multiple-valued neural network for pattern recognition and associative recall

A new neural network algorithm based on the counter-propagation network (CPN) architecture, named MVL-CPN, is proposed in this paper for bidirectional mapping and recognition of multiple-valued patterns. The MVL-CPN is capable of performing a mathematical mapping of a set of multiple-valued vector pairs by self-organization. The use of MVL-CPN reduces considerably the number of nodes required for the input layers as well as the number of synaptic weights compared to the binary CPN. The training of the network is stable because all synaptic weights are monotonically nonincreasing. The bidirectional mapping and associative recall features of the MVL-CPN are tested by using various sets of quaternary patterns. It is observed that the MVL-CPN can converge within three or four iterations. The high-speed convergence characteristics of the network can lead to the possibility of using this architecture for real-time applications. An important advantage of the proposed type of neural network is that it can be implemented in VLSI with reduced number of neurons and synaptic weights when compared to a larger binary network needed for the same application. (C) 2000 John Wiley & Sons, Inc.