Attractors and the attraction basins of discrete-time cellular neural networks

来源 :Journal of Systems Engineering and Electronics | 被引量 : 0次 | 上传用户:lingshi185
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The dynamic behavior of discrete-time cellular neural networks(DTCNN), which is strict with zero threshold value, is mainly studied in asynchronous mode and in synchronous mode. In general, a k-attractor of DTCNN is not a convergent point. But in this paper, it is proved that a k-attractor is a convergent point if the strict DTCNN satisfies some conditions. The attraction basin of the strict DTCNN is studied, one example is given to illustrate the previous conclusions to be wrong, and several results are presented. The obtained results on k-attractor and attraction basin not only correct the previous results, but also provide a theoretical foundation of performance analysis and new applications of the DTCNN. The dynamic behavior of discrete-time cellular neural networks (DTCNN), which is strictly with zero threshold value, is mainly studied in asynchronous mode and in synchronous mode. But in this paper, it is proved that a k-attractor is a convergent point if the strict DTCNN satisfies some conditions. one of the strict DTCNN is studied, one example is given to illustrate the previous conclusions to be wrong, and several results are The obtained results on k-attractor and attraction basin not only correct the previous results, but also provide a theoretical foundation of performance analysis and new applications of the DTCNN.
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