Abstract
The local asymptotical stability of bi-directional associative memory (BAM) neural networks with generalized saturation output functions is studied. By adopting the method of decomposing the state space to sub-regions and by using the technique of matrix norm, some delay-independent stability algebraic criteria are obtained, and the attractive domains are estimated. The results obtained in this paper need only to compute the norm of some matrices constructed by the parameters of the neural networks which are very convenient to verify in system synthesis.
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Chen, J., Xu, J. (2007). Locally Asymptotical Behaviors of Delayed BAM Neural Networks with Generalized Saturation Output Functions. In: Huang, DS., Heutte, L., Loog, M. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Contemporary Intelligent Computing Techniques. ICIC 2007. Communications in Computer and Information Science, vol 2. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74282-1_3
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DOI: https://doi.org/10.1007/978-3-540-74282-1_3
Publisher Name: Springer, Berlin, Heidelberg
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