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The exponential stability problem for complex-valued memristor-based recurrent neural networks (CVMRNNs) with time delays is studied in this paper. As an extension of real-valued memristor-based recurrent neural networks, CVMRNNs can… Click to show full abstract
The exponential stability problem for complex-valued memristor-based recurrent neural networks (CVMRNNs) with time delays is studied in this paper. As an extension of real-valued memristor-based recurrent neural networks, CVMRNNs can be separated into real and imaginary parts and an equivalent real-valued system is formed. By constructing a novel Lyapunov function, a new sufficient condition to guarantee the existence, uniqueness, and global exponential stability of the equilibrium point for complex-valued systems is given in terms of M-matrix. The effectiveness of the theoretical result is shown by two numerical examples.
Journal Title: Neural Computing and Applications
Year Published: 2017
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