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Signal processing using cellular neural networks

  • L. O. Chua
  • L. Yang
  • K. R. Krieg
Article

Abstract

The cellular neural network (CNN) architecture combines the best features from traditional fully-connected analog neural networks and digital cellular automata. The network can rapidly process continuous-valued (gray-scale) input signals (such as images) and perform many computation functions which traditionally were implemented in digital form. Here, we briefly introduce the the theory of CNN circuits, provide some examples of CNN applications to image processing, and discuss work toward a CNN implementation in custom CMOS VLSI. The role of analog computer-aided design (CAD) will be briefly presented as it relates to analog neural network implementation.

Keywords

Radon Input Image Cellular Automaton Noise Removal Feedback Operator 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Kluwer Academic Publishers 1991

Authors and Affiliations

  • L. O. Chua
    • 1
  • L. Yang
    • 1
  • K. R. Krieg
    • 1
  1. 1.Dept. of EECSUniversity of CaliforniaBerkeley

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