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Title: Complex dynamics of a delayed discrete neural network of two nonidentical neurons

In this paper, we discover that a delayed discrete Hopfield neural network of two nonidentical neurons with self-connections and no self-connections can demonstrate chaotic behaviors. To this end, we first transform the model, by a novel way, into an equivalent system which has some interesting properties. Then, we identify the chaotic invariant set for this system and show that the dynamics of this system within this set is topologically conjugate to the dynamics of the full shift map with two symbols. This confirms chaos in the sense of Devaney. Our main results generalize the relevant results of Huang and Zou [J. Nonlinear Sci. 15, 291–303 (2005)], Kaslik and Balint [J. Nonlinear Sci. 18, 415–432 (2008)] and Chen et al. [Sci. China Math. 56(9), 1869–1878 (2013)]. We also give some numeric simulations to verify our theoretical results.
Authors:
 [1] ;  [2] ;  [3]
  1. Mathematics Department, GuangDong University of Finance, Guangzhou 510521 (China)
  2. Mathematics Department, Texas A and M University at Qatar, P. O. Box 23874, Doha (Qatar)
  3. Mathematics Department, Sun Yat-Sen University, Guangzhou 510275, People's Republic China (China)
Publication Date:
OSTI Identifier:
22251655
Resource Type:
Journal Article
Resource Relation:
Journal Name: Chaos (Woodbury, N. Y.); Journal Volume: 24; Journal Issue: 1; Other Information: (c) 2014 AIP Publishing LLC; Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS; CHAOS THEORY; MAPS; NERVE CELLS; NEURAL NETWORKS; NONLINEAR PROBLEMS; SIMULATION; TOPOLOGY