Abstract
In this paper, we investigates synchronization dynamics of neural networks. Generalized linear synchronization (GLS) is proposed to acquire a general kind of proportional relationships between two-neuron networks. Under the point of synchronization, we can find that the node has complex dynamics with some interesting characteristics, and some new chaos phenomenons can been found. Numerical simulations show that this method works very well of two-neuron networks with identical Lorenz systems. Also our method can be applied to other systems. © 2008 Springer-Verlag Berlin Heidelberg.
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CITATION STYLE
Cheng, Z. (2008). New chaos produced from synchronization of chaotic neural networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5263 LNCS, pp. 40–46). Springer Verlag. https://doi.org/10.1007/978-3-540-87732-5_5
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