GLOBAL DISSIPATIVITY OF POSITIVE NEURAL NETWORKS WITH TIME-VARYING DELAYS

Authors

  • Le Nguyen Van Duyen K71 Student of the Faculty of Mathematics, Hanoi National University of Education, Hanoi city, Vietnam

DOI:

https://doi.org/10.18173/2354-1059.2025-0018

Keywords:

positive neural networks, dissipativity, time-varying delay, M-matrix

Abstract

In this paper, the problems of positivity and dissipativity are investigated for a model of neural networks with time-varying delays. A novel approach based on comparison techniques via differential and integral inequalities is presented and utilized to derive testable conditions which ensure the existence and the global dissipativity of a unique positive equilibrium point. The derived conditions are formulated in terms of linear programming with M-matrix, providing a computationally efficient framework for analysis. A numerical example with simulations is provided to illustrate the theoretical results and demonstrate their practical applicability.

References

[1] Souli F & Gallinari P, (1998). Industrial applications of neural networks. World Scientific Publishing, Singapore.

[2] Venketesh P & Venkatesan R, (2009). A survey on applications of neural networks and evolutionary techniques in web caching. IETE Technical Review, 26(3), 171–180.

[3] Raschman E, Zlusk R & Durakov D, (2010). New digital architecture of CNN for pattern recognition. Journal of Electrical Engineering, 61(4), 222–228.

[4] Huang G, Huang GB, Song S & You K, (2015). Trends in extreme learning machines: A review. Neural Networks, 61, 32–48.

[5] Zhang H, Wang Z & Liu D, (2014). A comprehensive review of stability analysis of continuous-time recurrent neural networks. IEEE Transactions on Neural Networks and Learning Systems, 25(7), 1229–1262.

[6] Baldi P & Atiya AF, (1995). How delays affect neural dynamics and learning. IEEE Transactions on Neural Networks, 5(5), 612–621.

[7] Lu H, (2012). Chaotic attractors in delayed neural networks. Physics Letters A, 298(2–3), 109–116.

[8] Park JH, Lee TH, Liu Y & Chen J, (2019). Dynamic systems with time delays: Stability and control. Springer-Nature, Singapore.

[9] Le VH & Doan TS, (2015). Finite-time stability of a class of non-autonomous neural networks with heterogeneous proportional delays. Applied Mathematics and Computation, 251, 14–23.

[10] Arik S, (2016). Dynamical analysis of uncertain neural networks with multiple time delays. International Journal of Systems Science, 47(4), 730–739.

[11] Lee TH, Trinh H & Park JH, (2018). Stability analysis of neural networks with time-varying delay by constructing novel Lyapunov functionals. IEEE Transactions on Neural Networks and Learning Systems, 29(9), 4238–4247.

[12] Ge C, Park JH, Hua C & Shi C, (2019). Robust passivity analysis for uncertain neural networks with discrete and distributed time-varying delays. Neurocomputing, 364, 330–337.

[13] He J, Liang Y, Yang F & Yang F, (2020). New H1 state estimation criteria of delayed static neural networks via the Lyapunov–Krasovskii functional with negative definite terms. Neural Networks, 123, 236–247.

[14] Smith H, (2008). Monotone Dynamical Systems: An Introduction to the Theory of Competitive and Cooperative Systems. American Mathematical Society, Providence.

[15] Mzaryn J & Kurek JE, (2010). Design of a neural network for an identification of a robot model with a positive definite inertia matrix. In: Artificial Intelligence and Soft Computing, Springer-Verlag, Berlin.

[16] Ma GJ, Wu S & Cai GQ, (2013). Neural networks control of the Ni-MH power battery positive mill thickness. Applied Mechanics and Materials, 411–414, 1855–1858.

[17] Ameloot TJ & Van den Bussche J, (2015). Positive neural networks in discrete time implement monotone-regular behaviors. Neural Computation, 27(12), 2623–2660.

[18] Abiodun OI, Jantan A, Omolara AE, Dada KV, Mohamed NA & Arshad H, (2018). State-of-the-art in artificial neural network applications: A survey. Heliyon, 4, e00938.

[19] Forti M & Tesi A, (1995). New conditions for global stability of neural networks with application to linear and quadratic programming problems. IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications, 42(5), 354–366.

[20] Berman A & Plemmons RJ, (1984). Nonnegative Matrices in the Mathematical Sciences. Society for Industrial and Applied Mathematics, Philadelphia.

[21] Arino O, Hbid ML & Ait Dads E, (2002). Delay Differential Equations and Applications. Springer, Dordrecht.

Downloads

Published

30-06-2025

How to Cite

Nguyen Van Duyen, L. (2025). GLOBAL DISSIPATIVITY OF POSITIVE NEURAL NETWORKS WITH TIME-VARYING DELAYS. Journal of Science Natural Science, 70(2), 23-36. https://doi.org/10.18173/2354-1059.2025-0018