Constrained differential evolution using generalized opposition-based learning

Differential evolution (DE) is a well-known optimization approach to deal with nonlinear and complex optimization problems. However, many real-world optimization problems are constrained problems that involve equality and inequality constraints. DE with constraint handling techniques, named constrained differential evolution (CDE), can be used to solve constrained optimization problems. In this paper, we propose a new CDE framework that uses generalized opposition-based learning (GOBL), named GOBL-CDE. In GOBL-CDE, firstly, the transformed population is generated using general opposition-based learning in the population initialization. Secondly, the transformed population and the initial population are merged and only half of the best individuals are selected to compose the new initial population to proceed mutation, crossover, and selection. Lastly, based on a jumping probability, the transformed population is calculated again after generating new populations, and the fittest individuals are selected to compose new population from the union of the current population and the transformed population. The GOBL-CDE framework can be applied to most CDE variants. As examples, in this study, the framework is applied to two popular representative CDE variants, i.e., rank-iMDDE and \(\varepsilon \)DEag. Experiment results on 24 benchmark functions from CEC’2006 and 18 benchmark functions from CEC’2010 show that the proposed framework is an effective approach to enhance the performance of CDE algorithms.     

Fault diagnosis and fault tolerant control for non-Gaussian time-delayed singular stochastic distribution systems

Stochastic distribution control (SDC) is a new branch of stochastic system control that the system output is the probability density function (PDF) of the output. In practice, some algebraic relations exist between the input and the weights of SDC systems, leading to a singular state space model between the weights and the control input which increases the complexity of the system. The ignorance of time delay in practical systems will make the effectiveness of the fault diagnosis (FD) and fault tolerant control (FTC) be reduced. In this paper, the linear B-spline basis functions are used to approximate the output PDF. A FD approach based on the adaptive observer is established to diagnose the size of fault in the singular time-delayed SDC system. With the fault diagnosis information, a fault tolerant controller based on PI tracking control scheme is constructed to make the post-fault PDF still track the given distribution. The post-fault closed-loop stability analysis with the practical fault tolerant controller is carried out based on the Lyapunov stability theorem. Finally, a numerical simulation is provided to demonstrate the effectiveness of the proposed approach.     

基于灰关联混合蛙跳算法的雷达波形设计

提出了一种基于灰关联混合蛙跳算法的雷达波形设计方法,以混合蛙跳算法为主体,在局部更 新算子中引入遗传算法的遗传算子,并改进原始蛙跳算法的分组方法,丰富了种群的多样性,同时引入灰关联综合评价法则对适应度函数值加以关联度分析。文中以设计具有低自相关旁瓣和互相关特性的正交多相编码为例,将该算法用于雷达波形设计中。仿真结果表明使用本文算法产生 的波形具备较好的低自相关特性和互相关特性,表明了该算法是有效和可行的。     

装备故障样本优化方法研究

测试性验证装备的故障样本往往相互关联,全部注入费用较高、代价较大.为了降低验证试验费用,采用适当方法对故障样本进行优化分析.为提高故障检测率,提出了一种等价样本的故障样本优化方法.方法在分析故障-测试关联矩阵及其扩展、故障模式功能等价集合和故障模式测试等价集合的基础上,构建了故障样本等价集合,并进行重要度特征分析和最大熵求解,确定了最小的故障样本集合.通过对某型试验台故障样本优化实例分析,并与传统的方法进行了试验结果对比分析,使得故障样本数量、试验费用大为减少,提高了测试性验证的经济性.     

浅谈动画广告设计中的图形创意思维

图形创意及其自身独特的艺术形式在动画广告中有着不可替代的优势,发挥着举足轻重的作用。本文通过动画广告设计与图形创意的关系研究,总结动画广告设计中几种重要的图形创意思维形式,并强调了图形创意思维形式在动画广告设计中的具体应用,如何把握图形创意思维的规律及其在动画广告中的实际价值是一个重要课题。     

Less conservative stability criteria for linear systems with interval time‐varying delays

The problem of the stability of a linear system with an interval time‐varying delay is investigated. A new Lyapunov–Krasovskii functional that fully uses information about the lower bound of the time‐varying delay is constructed to derive new stability criteria. It is proved that the proposed Lyapunov–Krasovskii functional can lead to less conservative results than some existing ones. Based on the proposed Lyapunov–Krasovskii functional, two stability conditions are developed using two different methods to estimate Lyapunov–Krasovskii functional's derivative. Two numerical examples are given to illustrate that the two stability conditions are complementary and yield a larger maximum upper bound of the time‐varying delay than some existing results. Copyright © 2013 John Wiley & Sons, Ltd.     

用能量法分析体外预压力对简支梁动力性能的二阶效应

精细地考虑了体外筋变形中的二阶项,用能量法推导了体外预应力梁的自振频率,阐明了体外预压力对梁自振频率的效应.计算结果表明:体外预压力压缩软化效应的影响系数,主要取决于转向座的数量.无转向座时,体外筋偏心距损失为最大,影响系数为1,体外预压力的效应与外轴力的效应相同.随着转向座数量的增加,偏心距损失减小,体外筋接近于无粘结筋,影响系数降低至接近于0,即接近于无粘结筋预压力的零效应.当梁转向座的数量≥2时,由于影响系数显著地小于1,可以忽略体外预压力的压缩软化效应.随着体外筋面积和偏心距的增加,梁的第1自振频率增大.不过,体外筋对其他阶自振频率的影响很小,可以忽略.     

Single event upset rate modeling for ultra-deep submicron complementary metal-oxide-semiconductor devices

Based on the integral method of single event upset(SEU) rate and an improved charge collection model for ultra-deep submicron complementary metal-oxide-semiconductor(CMOS) devices, three methods of SEU rate calculation are verified and compared. The results show that the integral method and the figure of merit(FOM) methods are basically consistent at the ultra-deep submicron level. By proving the validity of the carrier collection model considering charge sharing, the applicability of two FOM methods is verified, and the trends of single-bit and multiple-bit upset rates for ultra-deep submicron CMOS are analyzed.     

Distributed non-cooperative robust MPC based on reduced-order models

In this paper, a non-cooperative distributed MPC algorithm based on reduced order model is proposed to stabilize large-scale systems. The large-scale system consists of a group of interconnected subsystems. Each subsystem can be partitioned into two parts: measurable part, whose states can be directly measured by sensors, and the unmeasurable part. In the online computation phase, only the measurable dynamics of the corresponding subsystem and neighbour-to-neighbour communication are necessary for the local controller design. Satisfaction of the state constraints and the practical stability are guaranteed while the complexity of the optimization problem is reduced. Numerical examples are given to show the effectiveness of this algorithm.