This thesis’s object is inertial memristive neural networks (IMNNs) with proportional delays and switching jumps mismatch. Different from the traditional bounded delay, the proportional delay will be infinite as t → ∞. The finite-time synchronization (FN-TS) and fixed-time synchronization (FX-TS) can be realized with the devised controllers for the drive-response systems (D-RSs). Along with the Lyapunov function and some inequalities, the synchronization criteria of D-RSs are given. This paper presents an optimization model with minimum control energy and dynamic error as objective functions, aiming to obtain more accurate and optimized controller parameters. An intelligent algorithm: particle swarm optimization with stochastic inertia weight (SIWPSO) algorithm is introduced to solve the optimization model. Meanwhile, an integrated algorithm for selecting optimal control parameters is presented as well. In this method, the optimal control parameters and the setting time of synchronization can be obtained directly. At last, some simulations are presented to verify the theorems and the optimization model.

本文的目标是具有比例延迟和切换跳跃不匹配的惯性忆阻神经网络（IMNN）。从传统的有界延迟不同，比例延迟将是无限的作为吨→∞。可以使用为驱动响应系统（D-RS）设计的控制器来实现有限时间同步（FN-TS）和固定时间同步（FX-TS）。连同李雅普诺夫函数和一些不等式，给出了D-RS的同步标准。本文提出了一种以控制能量最小和动态误差为目标函数的优化模型，旨在获得更准确，优化的控制器参数。提出了一种智能算法：采用随机惯性权重的粒子群算法（SIWPSO）来求解优化模型。同时，提出了一种用于选择最优控制参数的集成算法。通过这种方法，可以直接获得最优的控制参数和同步的设置时间。最后，