A New Comparison Method for Stability Theory of Differential Systems with Time-Varying Delays

In this paper, a new comparison method is developed by using increasing and decreasing mechanisms, which are inherent in time-delay systems, to decompose systems. Based on the new method, whose expected performance is compared with the state of the original system, some new conditions are obtained to guarantee that the original system tracks the expected values. The locally exponential convergence rate and the convergence region of the polynomial differential equations with time-varying delays are also investigated. In particular, the comparison method is used to improve the 3/2 stability theorems of differential systems with pure delays. Moreover, the comparison method is applied to identify a threshold, and to consider the disease-free equilibrium points of an HIV endemic model with stages of progress to AIDs and time-varying delay. It is shown that if the threshold is smaller than 1, the equilibrium point of the model is globally, exponentially stable. Another application of the comparison method is to investigate the global, exponential stability of neural networks, and some new theoretical results are obtained. Numerical simulations are presented to verify the theoretical results.

[1]  R. May,et al.  Population biology of infectious diseases: Part II , 1979, Nature.

[2]  J. Hale Theory of Functional Differential Equations , 1977 .

[3]  Zhigang Zeng,et al.  Memory pattern analysis of cellular neural networks , 2005 .

[4]  M. Pituk Asymptotic behaviour of solutions of a differential equation with asymptotically constant delay , 1997 .

[5]  S. Grossberg Nonlinear difference-differential equations in prediction and learning theory. , 1967, Proceedings of the National Academy of Sciences of the United States of America.

[6]  On the stability for the delay-differential equation , 1986 .

[7]  K. Gopalsamy,et al.  Exponential stability of continuous-time and discrete-time cellular neural networks with delays , 2003, Appl. Math. Comput..

[8]  J J Hopfield,et al.  Neurons with graded response have collective computational properties like those of two-state neurons. , 1984, Proceedings of the National Academy of Sciences of the United States of America.

[9]  J. Hyman,et al.  An intuitive formulation for the reproductive number for the spread of diseases in heterogeneous populations. , 2000, Mathematical biosciences.

[10]  Lin-Bao Yang,et al.  Cellular neural networks: theory , 1988 .

[11]  Jinde Cao,et al.  An estimation of upperbound of delays for global asymptotic stability of delayed Hopfield neural networks , 2002 .

[12]  Jinde Cao,et al.  Global asymptotic stability of a general class of recurrent neural networks with time-varying delays , 2003 .

[13]  J. R. Haddock,et al.  On the delay-differential equations x′(t) + a(t)f(x(t − r(t))) = 0 and x″(t) + a(t)f(x(t − r(t))) = 0 , 1976 .

[14]  S. Arik An improved global stability result for delayed cellular neural networks , 2002 .

[15]  Toshiaki Yoneyama The 32 stability theorem for one-dimensional delay-differential equations with unbounded delay , 1992 .

[16]  J. Watmough,et al.  Reproduction numbers and sub-threshold endemic equilibria for compartmental models of disease transmission. , 2002, Mathematical biosciences.

[17]  Zhigang Zeng,et al.  Stability analysis of delayed cellular neural networks described using cloning templates , 2004, IEEE Trans. Circuits Syst. I Regul. Pap..

[18]  Jurang Yan,et al.  Stability theorems for nonlinear scalar delay differential equations , 2004 .

[19]  X. Liao,et al.  Absolute Stability of Nonlinear Control Systems , 2008 .

[20]  Jun Wang,et al.  Multiperiodicity and Exponential Attractivity Evoked by Periodic External Inputs in Delayed Cellular Neural Networks , 2006 .

[21]  Xianhua Tang,et al.  Global attractivity of non-autonomous Lotka-Volterra competition system without instantaneous negative feedback , 2003 .

[22]  S. Grossberg Global ratio limit theorems for some nonlinear functional-differential equations. II , 1968 .

[23]  Jin Xu,et al.  On the global stability of delayed neural networks , 2003, IEEE Trans. Autom. Control..

[24]  R. May,et al.  Population biology of infectious diseases: Part I , 1979, Nature.

[25]  Sabri Arik,et al.  An analysis of global asymptotic stability of delayed cellular neural networks , 2002, IEEE Trans. Neural Networks.

[26]  Carlos Castillo-Chavez,et al.  Stability and bifurcation for a multiple-group model for the dynamics of HIV/AIDS transmission , 1992 .

[27]  Seyed M. Moghadas,et al.  Global stability of a two-stage epidemic model with generalized non-linear incidence , 2002, Math. Comput. Simul..

[28]  Zhigang Zeng,et al.  Global Stability of a General Class of Discrete-Time Recurrent Neural Networks , 2005, Neural Processing Letters.

[29]  Jun Wang,et al.  Algebraic criteria for global exponential stability of cellular neural networks with multiple time delays , 2003 .

[30]  Xianhua Tang,et al.  3/2-type criteria for global attractivity of Lotka-Volterra competition system without instantaneous negative feedbacks $ , 2002 .

[31]  Zhigang Zeng,et al.  Global asymptotic stability and global exponential stability of neural networks with unbounded time-varying delays , 2005, IEEE Trans. Circuits Syst. II Express Briefs.

[32]  Jinde Cao,et al.  Global exponential stability and periodic solutions of recurrent neural networks with delays , 2002 .

[33]  Meifang Dong Global exponential stability and existence of periodic solutions of CNNs with delays , 2002 .

[34]  T. Yoneyama On the 32 stability theorem for one-dimensional delay-differential equations , 1987 .

[35]  J. Yorke Asymptotic stability for one dimensional differential-delay equations☆ , 1970 .

[36]  Wei Jing Stability of cellular neural networks with delay , 2007 .

[37]  Y. Hsieh,et al.  The effect of density-dependent treatment and behavior change on the dynamics of HIV transmission , 2001, Journal of mathematical biology.

[38]  Global stability for separable nonlinear delay differential systems , 2007 .

[39]  Zhigang Zeng,et al.  Complete stability of cellular neural networks with time-varying delays , 2006, IEEE Transactions on Circuits and Systems I: Regular Papers.

[40]  On Yoneyama's 32 Stability Theorems for One-Dimensional Delay Differential Equations , 2000 .

[41]  邓飞其,et al.  “Time Delays and Stimulus-Dependent Pattern Formation in Periodic Environments in Isolated Neurons’’的注记 , 2005 .