Abstract
Matrix-valued BAM neural networks are a generalization of real-valued BAM neural networks, for which the states, weights, and outputs are square matrices. This paper gives a sufficient criterion expressed in terms of linear matrix inequalities, for which the equilibrium point of these networks with time-varying delays is exponentially stable. A numerical example is provided to demonstrate the effectiveness of the proposed criterion.
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Popa, CA. (2017). Exponential Stability of Matrix-Valued BAM Neural Networks with Time-Varying Delays. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10636. Springer, Cham. https://doi.org/10.1007/978-3-319-70090-8_72
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DOI: https://doi.org/10.1007/978-3-319-70090-8_72
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