Version 1
: Received: 15 April 2020 / Approved: 16 April 2020 / Online: 16 April 2020 (13:33:43 CEST)
Version 2
: Received: 13 August 2020 / Approved: 20 August 2020 / Online: 20 August 2020 (04:13:36 CEST)
Version 3
: Received: 21 August 2020 / Approved: 22 August 2020 / Online: 22 August 2020 (09:55:22 CEST)
Version 4
: Received: 29 August 2020 / Approved: 31 August 2020 / Online: 31 August 2020 (08:26:12 CEST)
Version 5
: Received: 8 September 2020 / Approved: 11 September 2020 / Online: 11 September 2020 (08:27:42 CEST)
Version 6
: Received: 1 October 2020 / Approved: 2 October 2020 / Online: 2 October 2020 (13:57:27 CEST)
How to cite:
Rao, R. Stability Analysis of Nontrivial Stationary Solution and Constant Equilibrium Point of Reaction-Diffusion Neural Networks With Time Delays. Preprints2020, 2020040277. https://doi.org/10.20944/preprints202004.0277.v1
Rao, R. Stability Analysis of Nontrivial Stationary Solution and Constant Equilibrium Point of Reaction-Diffusion Neural Networks With Time Delays. Preprints 2020, 2020040277. https://doi.org/10.20944/preprints202004.0277.v1
Rao, R. Stability Analysis of Nontrivial Stationary Solution and Constant Equilibrium Point of Reaction-Diffusion Neural Networks With Time Delays. Preprints2020, 2020040277. https://doi.org/10.20944/preprints202004.0277.v1
APA Style
Rao, R. (2020). Stability Analysis of Nontrivial Stationary Solution and Constant Equilibrium Point of Reaction-Diffusion Neural Networks With Time Delays. Preprints. https://doi.org/10.20944/preprints202004.0277.v1
Chicago/Turabian Style
Rao, R. 2020 "Stability Analysis of Nontrivial Stationary Solution and Constant Equilibrium Point of Reaction-Diffusion Neural Networks With Time Delays" Preprints. https://doi.org/10.20944/preprints202004.0277.v1
Abstract
Firstly, the existence of asymptotically stable nontrivial stationary solution is derived by the comprehensive applications of Lyapunov-Razumikhin technique, design of state-dependent switching laws, a fixed point theorem, variational methods, and construction of compact operators on a convex set. This new theorem shows that the diffusion is a double-edged sword to the stability, refuting the views in previous literature that the greater the diffusion effect, the more stable the system will be. Next, a series of new theorems are presented one by one, which illustrates that the globally asymptotical stability of ordinary differential equations model for delayed neural networks may be locally stable in actual operation due to the inevitable diffusion. Besides, the non-zero constant equilibrium point is pointed out to be not the solution of delayed reaction diffusion system so that the stability of the non-zero constant equilibrium point of reaction diffusion system must lead to a contradiction. That is, non-zero constant equilibrium points are not in the phase plane of dynamic system. In addition, new theorems are further presented to prove that the small diffusion effect will cause the essential change of the phase plane structure of the dynamic behavior of the delayed neural networks, and thereby one equilibrium solution may become several stationary solutions, even infinitely many positive stationary solutions. Finally, a numerical example illustrates the feasibility of the proposed methods.
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
