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CHAOS IN TRANSIENTLY CHAOTIC NEURAL NETWORKS

CHAOS IN TRANSIENTLY CHAOTIC NEURAL NETWORKS
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摘要 It was theoretically proved that one-dimensional transiently chaotic neural networks have chaotic structure in sense of Li-Yorke theorem with some given assumptions using that no division implies chaos. In particular, it is further derived sufficient conditions for the existence of chaos in sense of Li- Yorke theorem in chaotic neural network, which leads to the fact that Aihara has demonstrated by numerical method. Finally, an example and numerical simulation are shown to illustrate and reinforce the previous theory. It was theoretically proved that one-dimensional transiently chaotic neural networks have chaotic structure in sense of Li-Yorke theorem with some given assumptions using that no division implies chaos. In particular, it is further derived sufficient conditions for the existence of chaos in sense of Li- Yorke theorem in chaotic neural network, which leads to the fact that Aihara has demonstrated by numerical method. Finally, an example and numerical simulation are shown to illustrate and reinforce the previous theory.
出处 《Applied Mathematics and Mechanics(English Edition)》 SCIE EI 2003年第8期989-996,共8页 应用数学和力学(英文版)
基金 the National Natural Science Foundation of China (70271065)
关键词 chaotic neural networks CHAOS no division chaotic neural networks chaos no division
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