This paper is mainly concerned with global exponential stability of time-varying delayed fuzzy inertial neural networks. Different from previous approaches of variable transformation, we use non-reduced order method. Different from previous non-reduced order method used to investigate the inertial neural networks without time-varying delays, we take the time-varying delayed effects into account. By constructing a modified delay-dependent Lyapunov functional and inequality technique, delay-dependent criteria stated with simple algebraic inequalities are given in order to ensure the global exponential stability for the addressed delayed fuzzy inertial neural network model. The approach applied can provide a new method to study the fuzzy inertial neural networks with time delays via non-reduced order method. Some previous works in the literature are extend and complement. Finally, numerical examples with simulations are presented to make comparisons between the system with delays and without delays, and further demonstrate the validity and originality of the proposed approach.

本文主要关注时变时滞模糊惯性神经网络的全局指数稳定性。与以前的变量转换方法不同，我们使用非降阶方法。与以前用于研究惯性神经网络且没有时变延迟的非降阶方法不同，我们考虑了时变延迟效应。通过构造改进的依赖于延迟的Lyapunov函数和不等式技术，给出了具有简单代数不等式的依赖于延迟的准则，以确保所解决的延迟模糊惯性神经网络模型的全局指数稳定性。通过非降阶方法可以为时滞模糊惯性神经网络的研究提供新的方法。文献中的某些先前著作是扩展和补充的。最后，给出了带有仿真的数值示例，以比较有延迟和无延迟的系统之间的差异，并进一步证明了该方法的有效性和独创性。