Associative memories with multi-valued cellular neural networks and their application to disease diagnosis

Abstract

Cellular neural networks (CNNs) are one type of interconnected neural network and differ from the well-known Hopfield model in that each cell has a piecewise linear output characteristic. In this paper, we present a multi-valued CNN model in which each nonlinear element consists of a multi-valued output function. The function is defined by a linear combination of piecewise linear functions. We conduct computer experiments of auto-associative recall to verify our multi-valued CNN's ability as an associative memory. In addition, we also apply our multivalued CNN to a disease diagnosis problem. The results obtained show that the multi-valued CNN improves classification accuracy by selecting the output level q properly. Moreover, these results also show that the multi-valued associative memory can expand both the flexibility of designing the memory pattern and its applicability.