Exponential stability analysis for delayed complex-valued memristor-based recurrent neural networks

@article{Zhang2017ExponentialSA,
  title={Exponential stability analysis for delayed complex-valued memristor-based recurrent neural networks},
  author={Ziye Zhang and Xiaoping Liu and Chong Lin and Shaowei Zhou},
  journal={Neural Computing and Applications},
  year={2017},
  volume={31},
  pages={1893-1903}
}
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… CONTINUE READING

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