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Passivity analysis for neural networks perturbed by Poisson noise and Gaussian noise

Neural networks (NNs) are always perturbed by random noise such as continuous noise and jump noise. This paper is concerned with the passivity problem of NNs perturbed by jump noise and continuous noise. Since Gaussian noise can depict continuous noise rather than jump noise, Poisson noise is utilized to give a description of jump noise. Then by employing the predictable projection operator and Lyapunov theory, a passivity criterion is presented by the linear matrix inequality (LMI) format. The main contribution of this paper is to extend the passivity research to NNs perturbed by continuous noise and jump noise. Two numerical examples are provided to illustrate the effectiveness of the proposed criterion.