scholarly journals Periodicity and stability analysis of impulsive neural network models with generalized piecewise constant delays

2021 ◽  
Vol 0 (0) ◽  
pp. 0-0
Author(s):  
Kuo-Shou Chiu ◽  
Keyword(s):  
Neural Network ◽  
Stability Analysis ◽  
Network Models ◽  
Neural Network Models ◽  
Piecewise Constant

Global exponential periodicity and stability of neural network models with generalized piecewise constant delay

2021 ◽  
Vol 71 (2) ◽  
pp. 491-512
Author(s):  
Kuo-Shou Chiu ◽  
Fernando Córdova-Lepe
Keyword(s):  
Neural Network ◽  
Exponential Stability ◽  
Global Exponential Stability ◽  
Sufficient Conditions ◽  
Network Models ◽  
Successive Approximations ◽  
Constant Delay ◽  
Neural Network Models ◽  
Piecewise Constant ◽  
Generalized Type

Abstract In this paper, the global exponential stability and periodicity are investigated for delayed neural network models with continuous coefficients and piecewise constant delay of generalized type. The sufficient condition for the existence and uniqueness of periodic solutions of the model is established by applying Banach’s fixed point theorem and the successive approximations method. By constructing suitable differential inequalities with generalized piecewise constant delay, some sufficient conditions for the global exponential stability of the model are obtained. Typical numerical examples with simulations are utilized to illustrate the validity and improvement in less conservatism of the theoretical results. This paper ends with a brief conclusion.

scholarly journals Stability Analysis of Neural Network Models in Engineering Design

2020 ◽  
Vol 9 (3) ◽  
pp. 1862-1866
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Stability Analysis ◽  
Engineering Design ◽  
Network Models ◽  
Control Problems ◽  
Neural Network Models ◽  
Optimization And Control ◽  
Efficient Criteria ◽  
And Control

In this paper neural networks applications in engineering design are discussed. The question for stability of their steady states is also considered. Some new efficient criteria are proposed. Since neural networks are relevant systems applied in various engineering design tasks, including many optimization and control problems, the results can be useful in design of such systems of diverse interest.

Existence and global exponential stability of equilibrium for impulsive neural network models with generalized piecewise constant delay

2021 ◽  
pp. 2250001
Author(s):  
Kuo-Shou Chiu
Keyword(s):  
Neural Network ◽  
Exponential Stability ◽  
Global Exponential Stability ◽  
Network Models ◽  
Cellular Neural Network ◽  
Impulsive Systems ◽  
Constant Delay ◽  
Neural Network Models ◽  
Piecewise Constant ◽  
Banach’S Fixed Point Theorem

In this paper, we investigate the models of the impulsive cellular neural network with generalized constant piecewise delay (IDEGPCD). To guarantee the existence, uniqueness and global exponential stability of the equilibrium state, several new adequate conditions are obtained, which extend the results of the previous literature. The method is based on utilizing Banach’s fixed point theorem and a new IDEGPCD’s Gronwall inequality. The criteria given are easy to check and when the impulsive effects do not affect, the results can be extracted from those of the non-impulsive systems. Typical numerical simulation examples are used to show the validity and effectiveness of the proposed results. We end the paper with a brief conclusion.

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