Abstract
In this paper, a class of reaction-diffusion recurrent neural networks with time-varying delays and Dirichlet boundary conditions are considered by using an approach based on the delay differential inequality and the fixed-point theorem. Some sufficient conditions are obtained to guarantee that the reaction-diffusion recurrent neural networks have a periodic orbit and this periodic orbit is globally attractive. The results presented in this paper are the improvement and extension of the existed ones in some existing works.
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Fu, C., Zhu, C., Chen, B. (2007). Periodicity of Recurrent Neural Networks with Reaction-Diffusion and Dirichlet Boundary Conditions. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4493. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72395-0_17
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DOI: https://doi.org/10.1007/978-3-540-72395-0_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72394-3
Online ISBN: 978-3-540-72395-0
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