A new definition of dissipativity for neural networks is presented in this paper.By constructing proper Lyapunov func-tionals and using some analytic techniques,sufficient conditions are given to ensure the dissipativity of neural networks with or without time-varying parametric uncertainties and the integro-differential neural networks in terms of linear matrix inequalities.Numerical examples are given to illustrate the effectiveness of the obtained results.
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机译:Discussion of 'Maximum Gradient Decision-Making for Railways Based on Convolutional Neural Network' by Hao Pu, Hong Zhang, Paul Schonfeld, Wei Li, Jie Wang, Xianbao Peng, and Jianping Hu