Continuous-time orbit problems are decidable in polynomial-time

We place the continuous-time orbit problem in P$\mathsf{P}$, sharpening the decidability result shown by Hainry [7].     

改进多种群差分进化算法的混沌系统参数估计

针对混沌系统参数估计的多峰寻优问题,提出一种改进的多种群差分进化算法。改进差分进化算法的变异操作,使其前期更适合全局性搜索,利用α核心集对当前种群进行聚类,分别对聚类后的子群选用贪婪的差分变异算子完成深度搜索,比较所选取各子群的最优值,得到全局最优值作为是否结束搜索的判断依据,并将其应用到混沌系统参数估计中。实验结果表明,该算法对于多峰值、大空间的全局性参数估计在收敛速度、精度上优于混合量子进化算法、改进粒子群优化算法以及DE/best/2算法。     

一类饱和非有理系统状态反馈设计

本文研究了一类饱和非有理系统状态反馈镇定问题.通过线性分式表示技术,这类非有理系统可以转化为带有两个非线性回路的线性时不变(linear time-invariant,LTI)系统.假设非有理函数项分别满足局部扇形区间不等式以及局部Lipschitz条件,提出了两种基于LMI条件的镇定方法.最后,举例证明了所提出方法的有效性.     

朴素差分进化算法

针对变异算子学习方式的单一性,提出一种朴素变异算子,其基本思想是向优秀的个体靠近,同时远离较差个体,其实现方式是设计一种缩放因子调整策略,如果三个随机个体在某维上比较接近,则缩放因子变小,反之变大.在实验过程中通过平均适应度评价次数、成功运行次数和加速比等指标表明,基于朴素变异算子的差分进化算法能有效提高算法的收敛速度和健壮性.     

蛋白质构象空间局部增强差分进化搜索方法

针对蛋白质构象空间搜索问题,提出一种蛋白质构象空间局部增强差分进化搜索方法。在差分进化算法框架下,采用Rosetta Score3粗粒度知识能量模型有效降低构象空间的搜索维数,加快算法收敛速度;引入基于知识的片段组装技术可以有效提高预测精度;利用Monte Carlo算法良好的局部搜索性能对种群做局部增强,以得到更为优良的局部构象;结合差分进化算法较强的全局搜索能力,可以对构象空间进行更为有效的采样。5个测试蛋白实验结果表明,所提算法具有较好的搜索性能和预测精度。     

Controller design and analysis for singularly perturbed switched systems with actuator saturation

This paper investigates the problems of controller design and stability analysis for singularly perturbed switched systems subject to actuator saturation. A set of well‐defined sufficient conditions for the existence of state feedback controllers is proposed, under which the closed‐loop system is locally asymptotically stable for arbitrary switching law as long as the singular perturbation parameter is sufficiently small. With the obtained controller, the estimation problem of stability bound and basin of attraction of the closed‐loop system is reduced to solving a convex optimization problem. A numerical example and a hydraulic servo position system are used to illustrate the obtained results. Copyright © 2016 John Wiley & Sons, Ltd.     

多螺旋桨太阳能无人机俯仰控制方法研究

在太阳能无人机飞行俯仰控制的研究中,为更好地满足太阳能无人机长航时的飞行要求,提出一种采用由多螺旋桨差动推力产生俯仰力矩从而达到俯仰控制效果的新型控制方法。太阳能无人机机翼的柔性结构特点造成飞行过程中螺旋桨产生纵向位移,为多螺旋桨差动控制提供了基础。由于多螺旋桨是过操纵机构,引入加权伪逆方法对多螺旋桨输入冗余进行控制分配,并与传统的升降舵LQR控制方法进行对比分析。仿真结果表明,多螺旋桨差动推力俯仰控制方法能达到良好的控制效果,且比传统升降舵控制更符合太阳能无人机飞行特点和可靠性要求,为太阳能无人机实现无气动舵面飞行控制提供了理论依据。     

Effects of Auditory Cues for Linear and Differential Magnification Methods on Visual Inspection Performance

The unfavorable consequence of biased attention to peripheral objects was reported with the use of a newly proposed differential magnification method by the authors. Given the favorable influence of auditory cueing on attention orientation, this study investigated the effectiveness of auditory cues in directing participants’ attention from the peripheral to foveal areas of the differential magnifying lens for improving inspection performance. Thirty‐nine Chinese undergraduates performed a simulated inspection task using a particular magnification method (between‐participant factor). Three within‐participant factors of auditory cue interval, target difficulty, and magnification power were tested. Objective performance measures of speed and accuracy and subjective evaluation using NASA task load index were collected and analyzed. The results showed that positive effects of auditory cues were found for the differential magnification methods but not for the traditional linear magnification method. Significant effects of target difficulty and magnification power were also found. It was concluded that the aid of auditory cues, which did not share the same attentional resource with the visual inspection task, proved to be an appropriate reminder for the differential magnification methods to improve participants’ visual inspection performance. Attention should also be paid to selecting an appropriate auditory cue interval for search tasks of different target difficulties. The findings of this study have implications for the design of video magnifiers in the context of multimodal human–machine interfaces.     

Multi-strategy ensemble particle swarm optimization for dynamic optimization

Optimization in dynamic environments is important in real-world applications, which requires the optimization algorithms to be able to find and track the changing optimum efficiently over time. Among various algorithms for dynamic optimization, particle swarm optimization algorithms (PSOs) are attracting more and more attentions in recent years, due to their ability of keeping good balance between convergence and diversity maintenance. To tackle the challenges of dynamic optimization, several strategies have been proposed to enhance the performance of PSO, and have gained success on various dynamic optimization problems. But there still exist some issues in dynamic optimization which need to be studied carefully, i.e. the robustness of the algorithm to problems of various dynamic features. In this paper, a new multi-strategy ensemble particle swarm optimization (MEPSO) for dynamic optimization is proposed. In MEPSO, all particles are divided into two parts, denoted as part I and part II, respectively. Two new strategies, Gaussian local search and differential mutation, are introduced into these two parts, respectively. Experimental analyses reveal that the mechanisms used in part I can enhance the convergence ability of the algorithm, while mechanisms used in part II can extend the searching area of the particle population to avoid being trapped into the local optimum, and can enhance the ability of catching up with the changing optimum in dynamic environments. The whole algorithm has few parameters that need to be tuned, and all of them are not sensitive to problems. We compared MEPSO with other PSOs, including MQSO, PHPSO and Standard PSO with re-initialization, on moving peaks Benchmark and dynamic Rastrigin function. The experimental results show that MEPSO has pretty good performance on almost all testing problems adopted in this paper, and outperforms other algorithms when the dynamic environment is unimodal and changes severely, or has a great number of local optima as dynamic Rastrigin function does.