Research Article
On Exponential Stability for Delayed Inertial BAM Neural Networks via Non-reduced Order Approach
3 downloads
@INPROCEEDINGS{10.1007/978-3-030-72792-5_21, author={Bingnan Tang and Bingjun Li and Jianjun Jiao and Fengjun Di}, title={On Exponential Stability for Delayed Inertial BAM Neural Networks via Non-reduced Order Approach}, proceedings={Simulation Tools and Techniques. 12th EAI International Conference, SIMUtools 2020, Guiyang, China, August 28-29, 2020, Proceedings, Part I}, proceedings_a={SIMUTOOLS}, year={2021}, month={4}, keywords={General BAM neural network Stability}, doi={10.1007/978-3-030-72792-5_21} }- Bingnan Tang
Bingjun Li
Jianjun Jiao
Fengjun Di
Year: 2021
On Exponential Stability for Delayed Inertial BAM Neural Networks via Non-reduced Order Approach
SIMUTOOLS
Springer
DOI: 10.1007/978-3-030-72792-5_21
Abstract
The present paper is studying a class of inertial BAM neural networks with general activations and delays. With the help of the non-reduced order method and designing some useful Lyapunov functions, criterions ensuring the exponential stability of the investigated network system are proposed, the obtained conditions are essentially new and complement previously stability results. Moreover, a simulated example is also presented in order to support the established fruits.
Copyright © 2020–2025 ICST
