Pattern memory analysis based on stability theory of cellular neural networks |
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| Authors: | Zhigang Zeng De-Shuang Huang Zengfu Wang |
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| Institution: | 1. School of Automation, Wuhan University of Technology, Wuhan, Hubei 430070, China;2. Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, Anhui 230031, China;3. Department of Automation, University of Science and Technology of China, Hefei, Anhui 230026, China |
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| Abstract: | In this paper, several sufficient conditions are obtained to guarantee that the n-dimensional cellular neural network can have even (?2n) memory patterns. In addition, the estimations of attractive domain of such stable memory patterns are obtained. These conditions, which can be directly derived from the parameters of the neural networks, are easily verified. A new design procedure for cellular neural networks is developed based on stability theory (rather than the well-known perceptron training algorithm), and the convergence in the new design procedure is guaranteed by the obtained local stability theorems. Finally, the validity and performance of the obtained results are illustrated by two examples. |
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| Keywords: | Stability Pattern memory Cellular neural networks Isolated equilibrium point |
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