College of Mathematics and System Sciences, Xinjiang University, Urumqi 830017, China.
College of Mathematics and System Sciences, Xinjiang University, Urumqi 830017, China; Xinjiang Key Laboratory of Applied Mathematics (XJDX1401), Urumqi, 830017, China.
Neural Netw. 2024 Dec;180:106669. doi: 10.1016/j.neunet.2024.106669. Epub 2024 Aug 29.
Inertial neural networks are proposed via introducing an inertia term into the Hopfield models, which make their dynamic behavior more complex compared to the traditional first-order models. Besides, the aperiodically intermittent quantized control over conventional feedback control has its potential advantages on reducing communication blocking and saving control cost. Based on these facts, we are mainly devoted to exploring of exponential synchronization of quaternion-valued inertial neural networks under aperiodically intermittent quantized control. Firstly, a compact quaternion-valued aperiodically intermittent quantized control protocol is developed, which can mitigate significantly the complexity of theoretical derivation. Subsequently, several concise criteria involving matrix inequalities are formulated through constructing a type of Lyapunov functional and employing a direct analysis approach. The correctness of the obtained results eventually is verified by a typical example.
惯性神经网络通过在 Hopfield 模型中引入惯性项来提出，这使得它们的动态行为比传统的一阶模型更复杂。此外，常规反馈控制的非周期性间歇量化控制在减少通信阻塞和节省控制成本方面具有潜在优势。基于这些事实，我们主要致力于研究非周期性间歇量化控制下四元数惯性神经网络的指数同步。首先，开发了一个紧凑的四元数非周期性间歇量化控制协议，这可以显著减轻理论推导的复杂性。随后，通过构造一种李雅普诺夫函数并采用直接分析方法，制定了几个包含矩阵不等式的简洁准则。最后，通过一个典型的例子验证了所得到的结果的正确性。
