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

• Published in: 2009 IEEE International Conference on Systems, Man and Cybernetics pp. 3824 - 3829
• Main Authors: Akiduki, T., Zhang Zhong, Imamura, T., Miyake, T.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.10.2009
• Subjects: Associative memory; Blood; Cellular neural networks; Design methodology; Diagnosis of disease; Hopfield neural networks; Inspection; Liver diseases; Multi-valued cellular neural networks; Neural networks; Piecewise linear techniques; Testing
• ISBN: 9781424427932; 1424427932
• ISSN: 1062-922X
• DOI: 10.1109/ICSMC.2009.5346618

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.