A constant-time neural network for multiple selection

Abstract

It is well known that one of the major advantage of neural networks lies in the constant time response once they are trained. However, if a neural network includes an MAXNET [5], a neural net for selection of maximum values, it usually cannot get the constant time response due to its drawback of inconsistent convergent time. Unfortunately, a neural network for classification often needs to use an MAXNET as a subnet generally.As a result, MAXNET can be a bottleneck in the recalling phase so that it is impractical to combine it with other neural networks. Most importantly, when MAXNET serves as an intermechary subnet. the neural network may be led to a wrong decision in the case that there are several largest values in the intermediate results, due to the random selection among those values of MAXNET. In this paper, an MAXNET with constant-time response and with miiltiple selection of several maximum values, CTMAXNET. is presented. The constant-time response for CTMAXNET is proved. The cost analysis of VLSI implementation for CTMAXNET is presented. It is compared with the cost of MAXNET in [6]. The result shows that CTMAXNET has less cost than MAX-NET. From the example of ARTI, it shows that including MAXNET leads to misclassification while including CTMAXNET does not. Through these results, a case is made that CTMAXNET is worthy of imbedding in a large number of neural network classifier models and applications.