Noise further expresses exponential decay for globally exponentially stable time-varying delayed neural networks.

Journal: Neural networks : the official journal of the International Neural Network Society
Published Date: Feb 21, 2016

Abstract

This paper shows that the globally exponentially stable neural network with time-varying delay and bounded noises may converge faster than those without noise. And the influence of noise on global exponential stability of DNNs was analyzed quantitatively. By comparing the upper bounds of noise intensity with coefficients of global exponential stability, we could deduce that noise is able to further express exponential decay for DNNs. The upper bounds of noise intensity are characterized by solving transcendental equations containing adjustable parameters. In addition, a numerical example is provided to illustrate the theoretical result.

Authors

  • Song Zhu
    College of Sciences, China University of Mining and Technology, Xuzhou, 221116, China. Electronic address: songzhu82@gmail.com.
  • Qiqi Yang
    College of Sciences, China University of Mining and Technology, Xuzhou, 221116, China. Electronic address: yangqiqikiki@163.com.
  • Yi Shen
    Department of Oral Maxillofacial-Head Neck Oncology, Ninth People's Hospital, College of Stomatology, Shanghai Jiao Tong University School of Medicine, National Clinical Research Center for Oral Diseases, Shanghai Key Laboratory of Stomatology & Shanghai Research Institute of Stomatology, Shanghai, China. Electronic address: shenyi_777@126.com.

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