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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Region Stabilization of Switched Neural Networks With Multiple Modes and Multiple Equilibria: A Pole Assignment Method
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Region Stabilization of Switched Neural Networks With Multiple Modes and Multiple Equilibria: A Pole Assignment Method

机译：具有多种模式的开关神经网络的区域稳定和多种均衡：极值分配方法

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摘要

This article investigates region stabilization issue of switched neural networks (SNNs) with multiple modes (MMs) and multiple equilibria (ME) via a pole assignment method. In such an SNN, every neuron is observed with more than one mode and unstable equilibrium point. First, SNNs with MMs and ME are modeled in terms of switched systems with unstable subsystems and ME. Second, a necessary and sufficient condition and a sufficient condition are, respectively, proposed for arbitrary switching paths pole assignment and arbitrary periodic/quasi-periodic switching paths (PSPs/QSPs) asymptotically region stabilizing pole assignment of switched linear time-invariant (LTI) systems with ME. It is shown that to stabilize a switched LTI system, some/all poles of all/some linear subsystems can be assigned to suitable locations of the right-half side of the complex plane. Third, based on the obtained pole assignment results, an asymptotical-region-stabilizing-control law observed as distributed state feedback controllers of MMs, asymptotical-region-stabilizing PSPs/QSPs, and a corresponding algorithm are all designed for asymptotical region stabilization of switched linearonlinear neural networks with MMs and ME. Finally, a numeral example is given to illustrate the effectiveness and practicality of the new results.
机译：本文通过极值分配方法调查具有多种模式（MMS）和多个均衡（ME）的切换神经网络（SNNS）的区域稳定问题。在这样的SNN中，用多个模式和不稳定的平衡点观察每个神经元。首先，使用MMS和ME的SNNS以具有不稳定子系统和ME的交换系统为模拟。其次，对于任意切换路径极限分配和任意周期/准周期性切换路径（PSP / QSP）渐近区域稳定极限分配切换线性时间不变（LTI）的任意周期/准周期性切换路径（PSP / QSP）呈现的必要和充分条件和足够的条件与我的系统。结果表明，为了稳定交换式LTI系统，可以将所有/一些线性子系统的一些/所有极点分配给复合平面右半侧的合适位置。第三，基于所获得的极点分配结果，观察到MMS，渐近区域稳定PSP / QSP的分布式状态反馈控制器的渐近区域稳定控制定律以及相应的算法都设计用于切换的渐近区域稳定化带MMS和ME的线性/非线性神经网络。最后，给出了数字示例来说明新结果的有效性和实用性。

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