The design of cellular neural network with ratio memory for pattern learning and recognition
Chung‐Yu Wu, Chiu-Hung Cheng · 2002
In this paper the cellular neural network (CNN) with ratio memory (RM) is implemented in CMOS to recognize and classify the image patterns. In the implemented CMOS CNN, the BJT-based combined four-quadrant multiplier and two-quadrant divider with separated magnitude and sign is used to implement the Hebbien learning function and the ratio memory. Thus, the combined multiplier and divider and the CNN have simple structure and large input/output signal range. The pattern learning and recognition function of the 9/spl times/9 CNN with RM is simulated by both Matlab software and HSPICE. It has been verified that the CNN with RM has the advantages of more stored patterns for processing and longer memory time with feature enhancement as compared to the CNN without RM. Thus, the proposed CNN with RM has great potential in the applications of neural associate memory for image processing.