PID参数优化方法对优化效果的影响研究

PID控制器对系统的经济和安全运行有着至关重要的影响,主要研究了PID参数优化方法对优化效果的影响.目前PID优化算法有多种,选取较典型的三种优化方法,经典优化方法(单纯形法)、智能优化方法(粒子群法)和专家系统三种优化方法进行分析与比较,通过实例分析讲述每种优化方法的优化原理,研究PID优化方法对系统性能的影响,得出三种优化方法优化后的参数和曲线,对它们进行分析比较,结果表明,三种优化方法在准确性、快速性、稳定性方面各有侧重点.     

基于约束满足的多目标双足机器人动态步态参数优化*

目前的步态优化算法仅仅实现了对单一目标的优化,把双足机器人步态优化看做是多目标优化问题,构建了衡量稳定性、能量消耗、步行速度三个目标评价函数。考虑到直接对多个目标加权求和的方法不能很好地处理多目标问题,提出一种新的基于约束满足的多目标步态参数优化算法,其思想是把基于惩罚函数的SPEA2(strength Pareto evolutionary algorithm2 )应用到多目标双足机器人动态步态参数优化问题上,规划出了同时满足这三个目标的动态优化步态。通过仿真实验表明了算法的有效性。     

基于串联协作的多方法协作优化方法

为更好地求解复杂优化问题的全局最优解，提出了基于串联协作的多方法协作优化方法。它采用串联方式组织各个优化方法之间的协作。各个优化方法在每次协作中进行若干次优化，每一优化方法在优化过程中及优化结果中包含的优化信息被用于其它优化方法中，以提高整体优化性能。选择了遗传算法、模式搜索法和Powell法等三个直接优化方法进行串联协作组成的多方法协作优化方法的设计，并对其优化特性进行了深入的理论分析。最后，用三个复杂多维函数对算法性能进行了测试。计算实例表明，基于串联协作的多方法协作优化方法取得了优于单个优化方法的全局最优特性。     

基于成本的MapReduce工作流优化器

对MapReduce栈的不同层进行优化有各自的优缺点。针对MapReduce工作负载的优化问题,提出了相关概念;通过与RoT的对比,介绍了MapReduce工作基于成本的优化及所使用的相关技术,并对MapReduce基于成本的优化进行了评估;基于工作流中的数据流依赖和资源依赖关系,提出了三种工作流优化器,评估了基于成本的工作流优化,并对工作流优化器进行了终端-对-终端的评估;通过实验评估了工作流优化器的优化开销并对这三种工作流优化器的优缺点进行了对比分析。     

Intel XScale处理器应用程序编写和优化

本文在介绍了Intel XScale嵌入式处理器特性的基础上，提出嵌入式系统设计中需要进行优化的三个目标——速度、空间和功耗，分析了三个目标之间的相互关系，并研究了针对XScale处理器进行优化的几种方法，取得了满意的效果。     

优化 还是外来的软件好念经

正观点系统优化的问题是老生常谈,杂志上也介绍了很多如何利用系统自带的组件进行优化的文章。为了节约系统资源,很多人不喜欢使用第三方软件,而是通过自己的技术手动对系统进行优化。但是,一些优化项目通过手动方法无法轻易实现,比如系统优化包括磁盘缓存、注册表、文件系统、网络、开机速度、系统安全、后台服务等优化项目,而系统组件一般只能针对某一个项目进行手动优化。另外,一些项目如注册表、磁盘缓存的优化,还需要掌握一定的电脑技术,具体执行起来并不容易。相比起来,第三方优化软件就简单多了,一般只要简单点击即可完成优化。     

基于三层交换技术的网络设计

本文通过对二层交换机、三层交换机工作原理的分析,从网络拓扑结构、网络带宽、网络优化三个方面探讨了构建三层交换以太网的设计。     

多机器人系统探索环境和创建地图的研究

提出了三种多机器人系统完成探索环境并创建地图任务的策略,采用免疫优化法的策略具有反应迅速、效率高的显著优点.通过仿真实验对三种策略进行了对比和评价,验证了免疫优化法的高效性.     

大数据分析在移动通信网络优化中的应用分析

现阶段,大数据发展得越来越好,其逐渐被应用到各行各业中。本文首先分析了大数据分析在移动通信网络优化中的应用的现状,然后从优化大数据储存能力、优化大数据处理能力、优化大数据分析能力这三方面提出了大数据分析在移动通信网络应用的优化策略。希望对相关人员带来启示,更科学的使用大数据进行移动网络优化。     

国内油田地面工程优化软件的技术问题初探

通过对国内油田地面工程规划优化、设计优化、运行优化软件的深入细致的调查分析,研究了油田地面工程优化软件的技术现状,找出了当前该项技术存在的技术问题。同时通过分析当前国内油田地面工程领域与优化软件有关的发展趋势,提出了未来十到二十年内发展该项技术需要开展的三项研究课题,为油田优化软件技术的宏观规划和管理决策提供了科学的依据。     

大规模工业过程的稳态递阶控制综述

连续运行的大规模工业过程或加工过程,其递阶控制理论有三个发展阶段:静态多级优 化、稳态递阶优化、系统优化及参数估计的综合方法等.本文评述了这三方面的主要成就并对 今后的重要研究方向提出了展望.     

非线性大系统的跟踪具有不同幅值轨线的分散型迭代学习控制器

在大型工业过程递阶稳态优化中, 可行的方法是利用系统的实际信息以修正基于模型的最优解. 在这种情形下, 得出一幅值不等的阶跃型控制值序列, 而且该控制值序列依次激励实际系统. 本文将一组迭代学习控制器分散地嵌入到一类非线性工业过程的递阶稳态优化进程中, 每一子系统的迭代学习控制器将产生一强化的控制信号序列以替代相应的具有不同幅值的阶跃型控制值序列, 目的是不断改进系统的暂态品质. 通过卷积的 Hausdorff-Young 不等式, 本文分析了学习控制律在 Lebesgue-P 范数意义下的收敛性, 讨论了系统的非线性性和关联性对控制律收敛性的影响. 最后, 数字仿真验证了所研究的学习控制机理的正确性和有效性.     

Iterative learning controllers for discrete-time large-scale systems to track trajectories with distinct magnitudes

In the procedure of steady-state hierarchical optimization for large-scale industrial processes, it is often necessary that the control system responds to a sequence of step function-type control decisions with distinct magnitudes. In this paper a set of iterative learning controllers are de-centrally embedded into the procedure of the steady-state optimization. This generates upgraded sequential control signals and thus improves the transient performance of the discrete-time large-scale systems. The convergence of the updating law is derived while the intervention from the distinction of the scales is analysed. Further, an optimal iterative learning control scheme is also deduced by means of a functional derivation. The effectiveness of the proposed scheme and the optimal rule is verified by simulation.     

基于神经网络的工业大系统辨识及稳态递阶优化方法

为了对工业大系统进行稳态递阶优化,必须首先获得系统的稳态模型.从神经网络的分 析人手,给出了工业大系统稳态模型的动态辨识方法及基于神经网络模型的推导方法.为了 提高算法的收敛速度,引入Lagrange函数解决大系统优化问题中的各种约束,并用Hopfield 网络实现了大系统稳态递阶优化的网络算法,最后给出了某一大系统辨识及优化的仿真结果.     

基于工序指标耦合关系分解的选冶全流程分层优化方法

针对某选冶全流程分层优化中工序指标众多且各工序指标之间存在一定耦合关系的问题,提出一种基于工序指标耦合关系分解的全流程分层优化方法.该方法是在全流程分层优化的基础上,利用两个指标实现对工序指标的分解,进而将工序层优化问题转换为相应的子优化问题.这种方法能够简化原始的工序层优化问题,提高选冶全流程分层优化问题的求解效率,且具有较高的全局收敛性.仿真结果表明,相比于常规全流程优化方法以及单纯的全流程分层优化方法,所提出的优化方法具有较好的性能.     

Filter-based iterative learning control for linear large-scale industrial processes

In the procedure of the steady-state hierarchical optimization with feedback for large-scale industrial processes, a sequence of set-point changes with different magnitudes is carried out on the optimization layer. To improve the dynamic performance of transient response driven by the set-point changes, a filter-based iterative leaning control strategy is proposed. In the proposed updating law, a local-symmetric-integral operator is adopted for eliminating the measurement noise of output information, a set of desired trajectories are specified according to the set-point changes sequence, the current control input is iteratively achieved by utilizing smoothed output error to modify its control input at previous iteration, to which the amplified coefficients related to the different magnitudes of set-point changes are introduced. The convergence of the algorithm is conducted by incorporating frequency-domain technique into time-domain analysis. Numerical simulation demonstrates the effectiveness of the proposed strategy.     

Optimal design of a three tape-spring hinge deployable space structure using an experimentally validated physics-based model

An optimal design approach is developed for a self-driven, self-locking tape-spring under a pure bending load in deployable space structures. A novel hinge with three tape springs is investigated and designed via an optimization process. Firstly, we investigate the steady-state moment and maximum stress of the hinge during deploying and folding processes using physics-based simulations. Experimental analyses are then conducted to verify the physics-based simulation results. Secondly, a parametric analysis is carried out to prove that both the tape spring thickness and subtended angle have significant effect on steady-state moment. A Response Surface Methodology (RSM) is employed to define an optimal surrogate model aimed at maximizing the steady-state moment, subjected to allowable stress. Finally, the Large Scale Generalized Reduced Gradient (LSGRG) optimization algorithm is used to solve the optimal design problem. Optimization results show that steady-state moment is increased by 19.5% while satisfying a maximum stress constraint. The proposed method is promising for designing novel deployable structures with high stability and reliability.     

双层结构预测控制研究进展

现代工业大系统的优化控制采用递阶结构,其中以预测控制为代表的先进过程控制已经成为重要的一级.目前,主流的工业预测控制技术均采用双层结构,即包含稳态优化层和动态控制层.双层结构预测控制技术可以有效解决复杂工业过程常见的多目标优化、多变量控制的难点问题.本文简要总结了双层结构预测控制的算法,并从控制输入与被控输出稳态关系入手分析了多变量预测控制稳态解的相容性和唯一性,说明了稳态优化的重要性.针对双层结构预测控制与区间预测控制的性能比较、稳态模型的奇异性以及闭环系统动态特性等提出了一些见解,并指出了需要重点研究的主题.     

Optical character recognition errors and their effects on natural language processing

Errors are unavoidable in advanced computer vision applications such as optical character recognition, and the noise induced by these errors presents a serious challenge to downstream processes that attempt to make use of such data. In this paper, we apply a new paradigm we have proposed for measuring the impact of recognition errors on the stages of a standard text analysis pipeline: sentence boundary detection, tokenization, and part-of-speech tagging. Our methodology formulates error classification as an optimization problem solvable using a hierarchical dynamic programming approach. Errors and their cascading effects are isolated and analyzed as they travel through the pipeline. We present experimental results based on a large collection of scanned pages to study the varying impact depending on the nature of the error and the character(s) involved. This dataset has also been made available online to encourage future investigations.     

Adaptive on-line steady-state optimization of slow dynamic processes

A software package OLIOPT was developed for the on-line optimization of the steady-state behaviour of slow dynamic processes in a relatively short time period. In the starting phase, the independently variable inputs are changed according to a special test signal. A nonlinear dynamic process model is identified on-line. Based on the static part of the model and the known inputs, the gradients of the performance index are calculated. An optimization algorithm changes the inputs towards their optimal values. On-line identification of the nonlinear model continues and the prediction of the optimum improves. In the last phase, the inputs take their optimal values and the process follows, feedforward controlled, to its optimal steady-state. The method is suited for industrial processes with one or more variable inputs, where a small gain in efficiency turns out to give a relatively large financial return. Results are shown for the on-line optimization of a thermal pilot process.