Robust dissipativity and passivity of stochastic Markovian switching CVNNs with partly unknown transition rates and probabilistic time-varying delay

AIMS Mathematics

article

Qiang Li¹  Weiqiang Gong²  Linzhong Zhang¹  Kai Wang¹ 

[1] School of Science, Anhui Agricultural University;School of Applied Mathematics, Nanjing University of Finance and Economics

关键词: complex-valued neural networks;    dissipativity;    Markovian switching;    partly unknown transition rates;    probabilistic time-varying delay;   

DOI  :  10.3934/math.20221068

学科分类：地球科学(综合)

来源: AIMS Press

【 摘 要 】

This article addresses the robust dissipativity and passivity problems for a class of Markovian switching complex-valued neural networks with probabilistic time-varying delay and parameter uncertainties. The main objective of this article is to study the proposed problem from a new perspective, in which the relevant transition rate information is partially unknown and the considered delay is characterized by a series of random variables obeying bernoulli distribution. Moreover, the involved parameter uncertainties are considered to be mode-dependent and norm-bounded. Utilizing the generalized It$ \hat{o} $'s formula under the complex version, the stochastic analysis techniques and the robust analysis approach, the $ (M, N, W) $-dissipativity and passivity are ensured by means of complex matrix inequalities, which are mode-delay-dependent. Finally, two simulation examples are provided to verify the effectiveness of the proposed results.

【 授权许可】

CC BY