A New Design Method of Multi-Valued Cellular Neural Networks for Associative Memory

It has been reported in the literature that cellular neural networks (CNN) are effective as associative memories and they have been applied to many kinds of pattern recognition tasks. Flexibility of their design can be increased by expanding the output function from being 2-valued to being multi-valued. As a design method for associative memories, SVD (singular value decomposition) is popular, but a design procedure which uses LMI (linear matrix inequality) was proposed and obtained excellent results. In this paper, we propose a new design method expanded from the 2-valued output CNN to a multi-valued output CNN by using the LMI method and confirm the effectiveness of it.