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New Stability Criteria for high-order neural networks with Proportional Delays

Communications in Theoretical Physics 2017年第3期67卷 235-240页

作者：徐昌进李培峦 Guizhou Key Laboratory of Economics System Simulation Guizhou University of Finance and Economics School of Mathematics and Statistics Henan University of Science and Technology

This paper is concerned with high-order neural networks with proportional delays. The proportional delay is a time-varying unbounded delay which is different from the constant delay, bounded time-varying delay and distributed delay. By the nonlinear transformation yi(t) = ui( et)(i = 1, 2,..., n), we transform a class of high-order neural networks with proportional delays into a class of high-order neural networks with constant delays and timevarying coefficients. With the aid of Brouwer fixed point theorem and constructing the delay differential inequality, we obtain some delay-independent and delay-dependent sufficient conditions to ensure the existence, uniqueness and global exponential stability of equilibrium of the network. Two examples with their simulations are given to illustrate the theoretical findings. Our results are new and complement previously known results.

关键词： high-order neural networks exponential stability proportional delays delay differential inequality Brouwer fixed point theorem

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Stability Analysis for Stochastic Delayed high-order neural networks

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Journal of Donghua University(English Edition) 2006年第1期23卷 73-77页

作者：舒慧生吕增伟魏国亮 College of Sciences Donghua UniversityShanghai 200051 College of Information Sciences and Technology Donghua UniversityShanghai 200051

In this paper, the global asymptotic stability analysis problem is considered for a class of stochastic high-order neural networks with ***. Based on a Lyapunov-Krasovskii functional and the stochastic stability analysis theory, several sufficient conditions are derived in order to guarantee the global asymptotic convergence of the equilibtium paint in the mean square. Investigation shows that the addressed stochastic highorder delayed neural networks are globally asymptotically stable in the mean square if there are solutions to some linear matrix inequalities （LMIs）. Hence, the global asymptotic stability of the studied stochastic high-order delayed neural networks can be easily checked by the Matlab LMI toolbox. A numerical example is given to demonstrate the usefulness of the proposed global stability criteria.

关键词： high-order neural networks stochastic systems time delays Lyapunov-Krasovskii functional global asymptotic stability linear matrix inequality

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Convergence Behavior of high-order Fuzzy Cellular neural networks with Time-varying Delays

Convergence Behavior of High-order Fuzzy Cellular Neural Net...

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第25届中国控制与决策会议

作者： Shuping Xu Manchun Tan Huimin Liao Department of Mathematics Jinan University

ISBN: (纸本)9781467355339

In this paper, the convergence behaviors are considered for a class of high-order fuzzy cellular neural networks with time-varying *** sufficient conditions are established to ensure that all solutions of the networks converge to zero point. Compared with some earlier works, the new criteria do not require the Lipschitz continuous condition on activation functions and the differentiability of variable delays. Finally, an example with numerical simulation is given to show the effectiveness of the proposed method and results.

关键词： high-order neural networks Convergence Delays Time-varying coefficients.

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