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

Content Provider	IEEE Xplore Digital Library
Author	Akiduki, T. Zhang Zhong Imamura, T. Miyake, T.
Copyright Year	2009
Description	Author affiliation: Department of Production Systems Engineering, Toyohashi University of Technology, Aichi, Japan (Akiduki, T.; Zhang Zhong; Imamura, T.; Miyake, T.)
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.
Starting Page	3824
Ending Page	3829
File Size	310172
Page Count	6
File Format	PDF
ISBN	9781424427932
ISSN	1062922X
DOI	10.1109/ICSMC.2009.5346618
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-10-11
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Associative memory Cellular neural networks Piecewise linear techniques Design methodology Neural networks Hopfield neural networks Liver diseases Blood Testing Inspection Diagnosis of disease Multi-valued cellular neural networks
Content Type	Text
Resource Type	Article

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