不确定分数阶神经网络系统状态估计的序相关抽样控制

Chao Ge, Qi Zhang, Hong Wang, Lei Wang
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引用次数: 0

摘要

本文利用采样数据控制器研究了一类具有不确定性的分数阶神经网络系统的状态估计问题。首先,考虑到数字领域的抗干扰性、不受噪声影响等方便性,针对采样时间可变的分数阶不确定神经网络系统设计了一种新的采样数据控制器。根据输入延迟方法,利用延迟系统对分数阶采样数据控制系统进行了仿真。该方法的主要目的是获得一个采样数据控制器增益K $$ K $$来估计神经元的状态,从而保证闭环分数阶系统的渐近稳定性。然后,利用分数阶Razumishin定理和线性矩阵不等式(lmi)推导出稳定条件。以lmi的形式给出了改进的时滞相关稳定条件和序相关稳定条件。此外,采样数据控制器可以保证分数阶系统的稳定性和镇定性。最后,给出了两个数值算例,验证了所提方法的有效性和优越性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Order-dependent sampling control for state estimation of uncertain fractional-order neural networks system

Order-dependent sampling control for state estimation of uncertain fractional-order neural networks system
In this paper, the problem of state estimation for a fractional-order neural networks system with uncertainties is studied by a sampled-data controller. First, considering the convenience of digital field, such as anti-interference, not affected by noise, a novel sampled-data controller is designed for the fractional-order neural network system of uncertainties with changeable sampling time. In the light of the input delay approach, the sampled-data control system of fractional-order is simulated by the delay system. The main purpose of the presented method is to obtain a sampled-data controller gain K $$ K $$ to estimate the state of neurons, which can guarantee the asymptotic stability of the closed-loop fractional-order system. Then, the fractional-order Razumishin theorem and linear matrix inequalities (LMIs) are utilized to derive the stable conditions. Improved delay-dependent and order-dependent stability conditions are given in the form of LMIs. Furthermore, the sampled-data controller can be acquired to promise the stability and stabilization for fractional-order system. Finally, two numerical examples are proposed to demonstrate the effectiveness and advantages of the provided method.
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