基于BP神经网络的战时维修人员编组评估模型研究

简要介绍了装备保障力量编组的基本方式,分析了维修人员编组的主要影响因素,针对维修人员编组方案评估过程中各因素组合的非线性及评估对象结构的复杂性,提出将维修分队成员同一属性的组合看成维修分队整体的一个属性,并利用BP神经网络对维修分队每个组合属性进行评估,最后利用线性加权法求出维修分队人员编组方案的综合评估结果。以某型火炮维修分队为例对该模型进行了运用,结果表明该模型具有客观、准确、简便的特点。研究结果可对维修人员的优化编组提供理论指导。     

基于概率论和自适应遗传算法的智能抽题算法

给出了一种基于概率论和自适应遗传算法的智能抽题算法的数学模型。该算法首先以概率论为基础优化初始参数，然后用自适应遗传算法对抽题进行操作，优化搜索过程，有效地解决了试题库中的智能组卷问题，具有较好的性能和实用性。     

目标选择的最近邻优化算法

坦克分队作战中，攻防双方多辆坦克的火力对抗涉及到火力分配，这时每辆坦克往往向威胁自身最大的目标开火，但这种目标选择的整体效果并不是最优的。由于最优火力分配是整数规划问题，当坦克分队中坦克数量较大时，求解最优的火力分配存在时间长和计算复杂等问题。因此在作战仿真或者指挥自动化系统中，对于这一问题多采用次优算法处理。该文提出一种最近邻目标选择优化算法，使得最大限度地减轻了对方对己方(坦克分队)的威胁，从而提高了坦克作战效能。     

基于性能合同的多部件系统维修策略模型优化

针对多部件串联系统维修效率低下与供应商所获利润少的问题，考虑各部件间的经济相关性，提出基于性能合同的多部件系统的维修策略模型。首先，采用韦伯（Weibull）分布描述系统各部件的使用寿命规律，并通过判断各部件使用度与预防性维修阈值和机会维修阈值的关系来实施不同的维修策略；其次，计算单位更新周期内的各维修活动概率和对应维修次数，以供应商利润最大化为目标，以预防性维修阈值和机会维修阈值为决策变量，建立基于性能合同的多部件系统的维修策略模型；最后，利用灰狼优化（GWO）算法求解该模型。通过算例分析得出与遗传（GA）算法、粒子群优化（PSO）算法相比，灰狼优化算法在准确率上分别提高了22.6%和7.6%；基于线性收益函数，所提绩效模型的利润率高达25.3%，相较于传统成本模型提高了5.2%。基于性能合同的多部件系统维修策略优化模型和算法可以有效解决供应商维修质量与效率低下问题，为供应商和运营商共同制定维修合同提供依据。     

一种智能组卷算法的实现

本文首先建立组卷问题的数学模型，通过分析可知，组卷问题实质上是一个多目标优化问题的求解。为了解决组卷这个多目标优化问题的求解方法．采用了一种遗传算法的智能组卷算法。     

基于时间延迟的多类型维修与经济生产批量联合优化研究

针对目前未能较好的综合考虑多维修类型与经济生产批量联合优化的问题，首先考虑多类型维修关系，基于时间延迟理论求出故障和缺陷次数的表达式；其次，在此基础上，综合考虑生产费用和维修费用的基础上，构建了多类型维修和经济生产批量联合优化模型，以单位时间内总费用最小为优化目标，获得最优检查间隔期和经济生产批量；最后，通过算例分析验证了模型的有效性，说明了第一类缺陷检查次数的多少对费用和经济生产批量影响不大。     

装备维修保障资源配置综述

装备维修资源配置优化方法的研究对提升维修保障效率具有不可估量的作用,然而当前装备维修保障系统的维修资源的研究已经不能满足多中心系统高效保障的实际需求。因此,本文详细分析了装备维修保障维修资源配置优化问题的相关研究。首先概述了多中心装备保障系统主要包括维修任务预测、维修资源配置和多中心维修任务调度3个任务模块,并以此确定了维修保障资源配置流程。然后重点针对维修人员和维修备件资源配置优化问题参数、理论方法以及研究现状进行了综述。最后针对多中心维修保障系统资源研究的缺陷对未来发展趋势进行了展望,形成一种新的研究思路,为后续开展多中心系统资源配置优化研究提供参考。     

虚拟维修拆卸序列规划方法研究

提出一种混沌遗传算法来解决了虚拟维修拆卸序列规划效率不高的问题.针对虚拟维修拆卸过程中拆卸序列规划问题,在遗传算法的基础上,结合混沌优化理论,提出了混沌遗传优化算法.上述算法以遗传算法为主流程,利用混沌现象不重复遍历的特点优化生成初始种群,然后对每次迭代中的个体以一定的概率进行混沌优化.经验证,混沌遗传算法能够有效解决虚拟维修拆卸序列优化问题.     

装备战时基地级维修作业分工优化

为快速完成装备修理任务,根据装备战时基地级维修的特点,提出了战时基地级装备维修作业分工的数学模型,在多工种多任务维修作业分工问题的基础上给出了带时间窗口的维修作业分工问题求解方案。根据该作业分工模型,利用遗传算法的特性,将问题背景与算法相融合,运用实例进行试验及分析。试验结果表明,本文提出的方法能有效地解决装备战时基地级维修作业分工优化问题。     

一种智能组卷算法的实现

本文首先建立组卷问题的数学模型,通过分析可知,组卷问题实质上是一个多目标优化问题的求解。为了解决组卷这个多目标优化问题的求解方法,采用了一种遗传算法的智能组卷算法。     

在逐点状态约束下一个四阶线性系统的时间最优控制

在逐点状态约束下,最优控制问题的求解是很困难的,已有的最大值原理和形态规划理论很难用来求解在逐点状态约束下最优控制问题.本文讨论逐点状态约束下一个四阶线性系统的时间最优控制问题.我们采用转换的方法给出了最优时间与最优控制的具体表达式.     

离散时间系统混合l1/H∞-控制问题

研究MIMO离散时间系统混合l₁/H_∞控制问题. 通过引入辅助优化问题, 证明了目标优化值关于约束H_∞指标的连续依赖性及其辅助问题最优化解的存在性, 同时证明了辅助优化问题的截断序列问题解的收敛性, 且其极限为混合l₁/H_∞_控制优化的辅助问题的最优化解. 还给出了优化目标值的上下界逼近.     

热传导方程的时间最优脉冲控制问题的有限元逼近

时间最优控制问题是一类典型的最优控制问题, 受到研究者的广泛关注. 脉冲控制是一种在工程控制中被广泛应用的控制方式. 偏微分方程描述系统的最优控制问题的数值逼近的收敛性为数值求解方法的可行性提供了定性依据. 本文研究热传导方程的时间最优脉冲控制问题的有限元逼近的收敛性. 通过利用投影算子的特性和系统状态的误差估计, 证明了逼近问题的最优时间收敛到原问题的最优时间. 由此进一步利用原问题最优控制的bangbang性证明了最优控制的收敛性.     

Lossless convexification of a class of optimal control problems with non-convex control constraints

We consider a class of finite time horizon optimal control problems for continuous time linear systems with a convex cost, convex state constraints and non-convex control constraints. We propose a convex relaxation of the non-convex control constraints, and prove that the optimal solution of the relaxed problem is also an optimal solution for the original problem, which is referred to as the lossless convexification of the optimal control problem. The lossless convexification enables the use of interior point methods of convex optimization to obtain globally optimal solutions of the original non-convex optimal control problem. The solution approach is demonstrated on a number of planetary soft landing optimal control problems.     

Discrete-time interval optimal control problem

This paper proposes a new approach for the discrete-time interval optimal control problem. The optimal interval solution of discrete-time interval optimal control problem is obtained using the single-level constrained interval arithmetic coupled with a dynamic programming technique. Moreover, the optimal interval solution contains the real-valued optimal solutions. To illustrate an optimal solution for the interval case, we use the minimax regret criterion in the interval inventory control problem we present.     

Optimal preposterior observation of dynamic systems

Consideration was given to the problem of optimal observation which accompanies the problem of optimal control under multiple uncertainties using feedforward, feedback and combined, closable and closed, loops. Sets of the initial and current preposterior distributions of the terminal state of the dynamic system were introduced. Problems of linear programming were formulated to evaluate these sets. Positional solution of the optimal observation problem which may be used for positional solution of the optimal control problem under multiple uncertainties was established relying on the current preposterior distribution. A method of positional solution of the optimal observation problem using the optimal estimator which in real time generates the current values of the positional solution was proposed. The results obtained were illustrated by an example.     

Discrete dynamic optimization of N-stages control for isolated signalized intersections

The N-stages control problem for isolated signalized intersections is defined as the control problem to disperse initial queue lengths to their optimal steady-state values in N cycles. Based on a discrete-event model of a simplified isolated signalized intersection, the N-stages control problem is formulated as a linear programming problem as well as a quadratic programming problem. A new algorithm is proposed for solving the discrete optimization problem by simple calculations, based on the optimal solution of the corresponding continuous-time problem. Numerical comparisons between the continuous-time optimal solution and the discrete-event optimal solutions, obtained from linear programming and sequential quadratic programming, are given for a few examples.     

A varying terminal time structure for stochastic optimal control under constrained condition

In this study, we propose a varying terminal time structure for the optimal control problem under state constraints, in which the terminal time follows the varying of the control via the constrained condition. Focusing on this new optimal control problem, we investigate a novel stochastic maximum principle, which differs from the traditional optimal control problem under state constraints. The optimal pair of the optimal control model can be verified via this new stochastic maximum principle.     

Parametric Filtering: Optimal Synthesis on a Nonlinear Dynamic Variety

We analyze the optimal parametric filtering problem for a wide class of nonlinear stochastic systems. Much attention is paid to validation of formed models of observation and estimation since derivation of equations that determine the mean-square optimal filter directly depends on a rational choice of the models. We find conditions under which the original optimal filtering problem is equivalent to the dual optimal control problem. Synthesis of the optimal filter is accompanied by the analysis of properties of solutions of filtering equations obtained.