一种改进的CVP方法及其在动态优化中的应用

针对控制向量参数化方法敏感度方程求解耗时长、时间节点数难确定等问题,提出一种改进的控制向量参数化方法．首先利用分段常数对系统敏感度方程进行近似处理,有效地得到了敏感度方程的近似解析解,避免了对高维敏感度方程数值积分的计算负担;然后根据目标函数关于控制参数的敏感度来选择需要细化的控制参数,得到满足优化精度要求的最优时间节点数．针对非线性ＣＳＴＲ 的仿真研究验证了所提出算法的可行性和有效性．

     

一种改进的SMO及其在人脸识别中的应用

SMO算法是一种有效的SVM训练算法,但由于参与训练的数据大部分为非支持向量,仍然存在进一步优化的可能性。针对SMO算法的这个不足,提出了一种改进的SMO算法,并将该算法应用到人脸识别中。试验结果表明了算法的有效性。     

一种动态优化的无线信道速率控制方法

速率控制是无线视频传输过程中重要的控制方法;在无线网络中采用了基于TCP友好速率控制TFRC（TCP-Friendly Rate Control）的速率控制方法;提出了基于动态优化的无线视频传输多用户TFRC的速率控制方法。该方法利用了自适应编码调制特点;根据用户的信道状况;动态优化物理层发送速率;经过理论分析和仿真证明可以在总的链路速率不增加的情况下;动态地根据各个用户的信道状况变化情况;公平地分配和调整各个用户带宽;从而最大效率地使用现有链路及提高整体的用户满意度。     

一种改进的综合生产计划动态规划优化方法

提出了一种改进的综合生产计划动态规划优化方法。以1999年甘应爱主编的《运筹学》第227~230页给出的一类综合生产计划问题为研究对象,深入分析了原综合生产计划问题、数学优化模型、动态规划求解过程、计算方法存在的不足并提出了相应的改进措施。通过案例分析验证了所提方法的有效性。     

一种基于数据的核参数优化方法

SVM是一种基于核的学习方法,核及相关参数的选择对其性能有非常重要的影响,提出了一种数据依赖的最优核参数估计方法,通过角度切割样本集求解训练样本的近似凸包,以确定最优的核参数。实验结果表明,无论数据是否稠密,分布是否均匀,算法都可适用,该方法有较高的可行性与有效性。     

一种改进的药代动力学参数优化方法及其应用

基于药代动力学参数优化方法PKAIN人工免疫网络算法,提出了迭代分组并发单纯形算子,并实现了线性网络抑制函数以简化人工免疫网络的参数设置。为了加快算法的搜索速度和搜索精度,提出了新型的人工免疫网络单纯形混合算法（PKAIN_spx）,用人工免疫网络实现粗粒度全局搜索,随后用单纯形进行精确搜索。仿真实验对改进的PKAIN算法（PKAIN_in）、人工免疫网络单纯形混合算法（PKAIN_spx）以及PKAIN算法进行了比较分析,结果表明PKAIN_spx算法在药代动力学参数优化中取得良好的实验效果。     

一种改进细菌觅食优化算法及其在软测量建模中的应用

针对软测量建模中模型参数的优化需求,在分析细菌觅食优化算法(BFOA)和粒子群优化(PSO)算法的基础上,将二者有机结合,提出了一种新型细菌觅食粒子群混合优化算法(BSOA)。该算法将PSO粒子移动的思想引入BFOA,有效解决了BFOA趋向性操作中细菌位置更新的盲目性。将其分别用于典型函数的寻优与成品油研究法辛烷值最小二乘支持向量机(LSSVM)模型参数的优化,仿真结果表明:该方法有效增强了算法的全局寻优能力与收敛速度,并在一定程度上改善了模型的预测精度与泛化能力。     

一种细菌觅食算法的改进及其应用

针对原有细菌觅食算法收敛速度慢、计算量大的问题,首先通过改进细菌种群大小、细菌运动步长、引进迭代终止条件改进原有细菌觅食算法,然后将其应用到支持向量机的参数优化上。实验以Iris标准测试数据集为依托,以高斯核支持向量机中核参数γ和惩罚因子C为优化对象,分析了遗传算法、粒子群算法、原有的和改进后的细菌觅食算法的寻优性能,验证了将改进后的细菌觅食算法应用到支持向量机参数选择上具有优越性。     

Design parameterization and tool integration for CAD-based mechanism optimization

This paper presents an open and integrated tool environment that enables engineers to effectively search, in a CAD solid model form, for a mechanism design with optimal kinematic and dynamic performance. In order to demonstrate the feasibility of such an environment, design parameterization that supports capturing design intents in product solid models must be available, and advanced modeling, simulation, and optimization technologies implemented in engineering software tools must be incorporated. In this paper, the design parameterization capabilities developed previously have been applied to support design optimization of engineering products, including a High Mobility Multi-purpose Wheeled Vehicle (HMMWV). In the proposed environment, Pro/ENGINEER and SolidWorks are supported for product model representation, DADS (Dynamic Analysis and Design System) is employed for dynamic simulation of mechanical systems including ground vehicles, and DOT (Design Optimization Tool) is included for a batch mode design optimization. In addition to the commercial tools, a number of software modules have been implemented to support the integration; e.g., interface modules for data retrieval, and model update modules for updating CAD and simulation models in accordance with design changes. Note that in this research, the overall finite difference method has been adopted to support design sensitivity analysis.     

Parameter selection of support vector regression based on hybrid optimization algorithm and its application

Choosing optimal parameters for support vector regression （SVR） is an important step in SVR. design, which strongly affects the pefformance of SVR. In this paper, based on the analysis of influence of SVR parameters on generalization error, a new approach with two steps is proposed for selecting SVR parameters, First the kernel function and SVM parameters are optimized roughly through genetic algorithm, then the kernel parameter is finely adjusted by local linear search, This approach has been successfully applied to the prediction model of the sulfur content in hot metal. The experiment results show that the proposed approach can yield better generalization performance of SVR than other methods,     

Online adaptive least squares support vector machine and its application in utility boiler combustion optimization systems

Boiler combustion optimization is a key measure to improve the energy efficiency and reduce pollutants emissions of power units. However, time-variability of boiler combustion systems and lack of adaptive regression models pose great challenges for the application of the boiler combustion optimization technique. A recent approach to address these issues is to use the least squares support vector machine (LS-SVM), a computationally attractive machine learning technique with rather legible training processes and topologic structures, to model boiler combustion systems. In this paper, we propose an adaptive algorithm for the LS-SVM model, namely adaptive least squares support vector machine (ALS-SVM), with the aim of developing an adaptive boiler combustion model. The fundamental mechanism of the proposed algorithm is firstly introduced, followed by a detailed discussion on key functional components of the algorithm, including online updating of model parameters. A case study using a time-varying nonlinear function is then provided for model validation purposes, where model results illustrate that adaptive LS-SVM models can fit variable characteristics accurately after being updated with the ALS-SVM method. Based on the introduction to the proposed algorithm and the case study, a discussion is then delivered on the potential of applying the proposed ALS-SVM method in a boiler combustion optimization system, and a real-life fossil fuel power plant is taken as an instance to demonstrate its feasibility. Results show that the proposed adaptive model with the ALS-SVM method is able to track the time-varying characteristics of a boiler combustion system.     

基于动态罚函数法的协同优化算法

为保证协同优化的系统级优化存在可行域,采用动态罚函数法,将学科间一致性约束条件下的系统级优化问题转化为无约束优化问题,提出了应用更为普遍的学科间不一致信息的定义形式,对各种定义形式进行了分析比较,并利用该值构造动态罚因子的表达式.从增强算法可靠性的角度,使用遗传算法来取代系统级优化问题中基于梯度的优化算法,同时减少了对优化函数的连续性要求,利用减速器典型算例对该方法进行了验证,结果表明该方法具有良好的优化性能.     

实时控制网动态通信频度研究

基于控制系统对通信要求的现实,提出对控制网采取动态通信频度。并给出动态通信频度的一种实现方法,结合竞争使用信道和通信调度,动态跟随通信需求频度,提高通信实时性,提高通信实际效能。     

矿山变频矢量控制系统效率优化方案研究

针对矿山变频调速系统感应电动机采用恒定磁通控制,存在损耗严重、运行效率低的问题,提出了一种矿山变频矢量控制系统的效率优化控制方案。该方案在两相旋转d-q坐标系下,建立了考虑定、转子铜损和铁损的系统损耗模型,推导得到不同运行工况下电动机损耗与转子磁链之间的对应关系;为改善因弱磁运行而造成的系统动态响应过慢问题,通过优化转矩电流和磁链电流比重关系,获得电动机电磁转矩的快速响应和转速跟踪。基于DSP+FPGA控制系统搭建22kW三电平感应电动机调速系统实验平台,实验结果表明,该优化方案可以降低感应电动机矢量控制系统运行损耗,提高系统运行效率,并保证系统的稳态性能和动态响应性能。     

Generalized control method by state convergence for teleoperation systems with time delay

In this paper a generalized design and control method for teleoperation systems with communication time delay is presented. The design method is based on the state space formulation and it allows to obtain the control parameters for any teleoperation system where the master and the slave manipulators would be represented by nth-order linear differential equations. Through state convergence between the master and the slave, the control system allows the slave to follow the master inspite of the time delay. The method is also able to establish the desired dynamics of this convergence and the dynamics of the slave manipulator. Experimental results are presented showing the validity of the proposed design and control method.     

改进的变尺度混沌粒子群算法及其应用

针对粒子群算法(PSO)存在局部最优及后期收敛速度慢等问题,提出一种改进的变尺度混沌粒子群算法(IMCPSO).该算法初期,在整个解空间对最优粒子进行变尺度混沌扰动,以防止陷入局部最优;算法后期,则以最优粒子为中心引入变尺度混沌扰动,以提高算法收敛速度.当算法一旦陷入局部最优时,采用混沌粒子替代部分种群粒子以增加粒子多样性,使算法尽快跳出局部最优.基于benchmark测试函数的仿真结果表明,所提算法与基本粒子群算法(SPSO)和变尺度混沌粒子群算法(MCPSO)相比,具有明显好的搜索精度和收敛速度.最后,将该算法应用于电路故障诊断实验中的支持向量机参数优化问题,实验结果说明了其应用价值.     

粒子群算法研究及其在流体矢量装置控制上的应用

粒子群算法对系统的依赖程度低,不要求被优化函数具有可微、可导、连续的特性。应用理论推导的方法,证明了粒子群算法在多极值目标函数下的全局收敛和局部收敛的条件,并且分析了粒子群算法中各参数对算法局部收敛速度、全局收敛能力的影响。本文主要贡献包括:分析发现粒子初始位置均匀分布可以提高算法的全局收敛能力;提出最大值的方法决定算法局部收敛速度,该函数不随权重系数单调递减;最后,将基于粒子群算法应用于流体矢量装置控制器上,仿真结果验证了该设计方法的有效性。