The design of ratio-memory cellular neural network (RMCNN) with self-feedback template weight for pattern learning and recognition

Chiu Hung Cheng, Chung-Yu Wu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

In this paper, a new type of the ratio-memory cellular neural network (RMCNN) with spatial-dependent self-feedback A-template weights is proposed and designed to recognize and classify the black-white image patterns. In the proposed RMCNN, the combined four-quadrant multiplier and two-quadrant divider with separated magnitude and sign is used to implement the Hebbian learning function and the ratio memory. To enhance the capability of pattern learning and recognition from noisy input patterns, the Z-template and the spatial-dependent self-feedback weights in the template A are applied to the proposed new type of RMCNN. The pattern learning and recognition function of the 18×18 RMCNN is simulated by Matlab software. It has been verified that the advanced RMCNN has the advantages of more stored patterns for recognition, and better recovery rate as compared to the original RMCNN. Thus the proposed RMCNN has great potential in the applications of neural associate memory for image processing.

Original languageEnglish
Title of host publicationProceedings of the 7th IEEE International Workshop on Cellular Neural Networks and their Applications, CNNA 2002
EditorsRonald Tetzlaff
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages609-615
Number of pages7
ISBN (Electronic)981238121X
DOIs
StatePublished - 1 Jan 2002
Event7th IEEE International Workshop on Cellular Neural Networks and their Applications, CNNA 2002 - Frankfurt, Germany
Duration: 22 Jul 200224 Jul 2002

Publication series

NameProceedings of the IEEE International Workshop on Cellular Neural Networks and their Applications
Volume2002-January

Conference

Conference7th IEEE International Workshop on Cellular Neural Networks and their Applications, CNNA 2002
CountryGermany
CityFrankfurt
Period22/07/0224/07/02

Keywords

  • Capacitors
  • Cellular neural networks
  • Councils
  • Feedforward neural networks
  • Image processing
  • Image recognition
  • Leakage current
  • Neural networks
  • Neurofeedback
  • Pattern recognition

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    Cheng, C. H., & Wu, C-Y. (2002). The design of ratio-memory cellular neural network (RMCNN) with self-feedback template weight for pattern learning and recognition. In R. Tetzlaff (Ed.), Proceedings of the 7th IEEE International Workshop on Cellular Neural Networks and their Applications, CNNA 2002 (pp. 609-615). [1035102] (Proceedings of the IEEE International Workshop on Cellular Neural Networks and their Applications; Vol. 2002-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CNNA.2002.1035102