Related Experiment Video
Updated: Feb 10, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
Published on: December 15, 2023
Global exponential stability of multitime scale competitive neural networks with nonsmooth functions
1Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200030, PR China. lu-ht@cs.sjtu.edu.cn
Abstract:
In this paper, we study the global exponential stability of a multitime scale competitive neural network model with nonsmooth functions, which models a literally inhibited neural network with unsupervised Hebbian learning. The network has two types of state variables, one corresponds to the fast neural activity and another to the slow unsupervised modification of connection weights. Based on the nonsmooth analysis techniques, we prove the existence and uniqueness of equilibrium for the system and establish some new theoretical conditions ensuring global exponential stability of the unique equilibrium of the neural network. Numerical simulations are conducted to illustrate the effectiveness of the derived conditions in characterizing stability regions of the neural network.
Related Concept Videos
Introduction to Exponential Functions
Exponential Functions with Base e
Competition
Network Function of a Circuit
Global Climate Change
Exponential and Sinusoidal Signals

