Active micro-vibration control based on improved variable step size LMS algorithm

The contradiction of variable step size least mean square(LMS)algorithm between fast convergence speed and small steady-state error has always existed.So,a new algorithm based on the combination of logarithmic and symbolic function and step size factor is proposed.It establishes a new updating method of step factor that is related to step factor and error signal.This work makes an analysis from 3 aspects:theoretical analysis,theoretical verification and specific experiments.The experimental results show that the proposed algorithm is superior to other variable step size algorithms in convergence speed and steady-state error.

Sliding state stepping algorithm for solving impact problems of multi-rigid-body system with joint friction

Impact dynamics of multi-rigid-body systems with joint friction is considered. Based on the traditional approximate assumption dealing with impact problem, a general numerical method called the sliding state stepping algorithm is introduced. This method can avoid difficulties in solving differential equations with variable scale and its result can avoid energy inconsistency before and after impact from considering complexily of tangential sliding mode. An example is given to describe details using this algorithm.

Auxiliary principle and three-step iterative algorithms for generalized set-valued strongly nonlinear mixed variational-like inequalities

An auxiliary principle technique to study a class of generalized set-valued strongly nonlinear mixed variational-like inequalities is extended. The existence and uniqueness of the solution of the auxiliary problem for the generalized set-valued strongly nonlinear mixed variational-like inequalities are proved, a novel and innovative three-step iterative algorithm to compute approximate solution is constructed, and the existence of the solution of the generalized set-valued strongly nonlinear mixed variational-like inequality is shown using the auxiliary principle iterative sequences generated by the algorithm technique. The convergence of three-step is also proved.

Random Timestepping Algorithm with Exponential Distribution for Pricing Various Structures of One-Sided Barrier Options

The exponentially-distributed random timestepping algorithm with boundary test is implemented to evaluate the prices of some variety of single one-sided barrier option contracts within the framework of Black-Scholes model, giving efficient estimation of their hitting times. It is numerically shown that this algorithm, as for the Brownian bridge technique, can improve the rate of weak convergence from order one-half for the standard Monte Carlo to order 1. The exponential timestepping algorithm, however, displays better results, for a given amount of CPU time, than the Brownian bridge technique as the step size becomes larger or the volatility grows up. This is due to the features of the exponential distribution which is more strongly peaked near the origin and has a higher kurtosis compared to the normal distribution, giving more stability of the exponential timestepping algorithm at large time steps and high levels of volatility.

An Improved Binary Wolf Pack Algorithm Based on Adaptive Step Length and Improved Update Strategy for 0-1 Knapsack Problems

Binary wolf pack algorithm (BWPA) is a kind of intelligence algorithm which can solve combination optimization problems in discrete spaces.Based on BWPA, an improved binary wolf pack algorithm (AIBWPA) can be proposed by adopting adaptive step length and improved update strategy of wolf pack. AIBWPA is applied to 10 classic 0-1 knapsack problems and compared with BWPA, DPSO, which proves that AIBWPA has higher optimization accuracy and better computational robustness. AIBWPA makes the parameters simple, protects the population diversity and enhances the global convergence.

面向STEP-NC基于混合式遗传算法的工艺路线优化

为适应STEP-NC技术的发展需求,解决基于STEP-NC非线性工艺路线优化问题,在深入分析STEP-NC基于加工特征的数据模型基础上,探讨了工艺路线优化中存在的问题及其相关约束,提出采用基于操作优先图的智能搜索与遗传算法相结合的混合式算法。首先考虑加工操作排序中的强制性约束,采用基于操作优先图的智能搜索算法,获取多个可行的加工操作顺序,并将其作为遗传算法的初始化种群;然后综合考虑机床选择、刀具选择和刀具运动方向选择,以加工成本最低为目标函数,根据优化约束条件,通过遗传算法的复制、交叉和变异等操作实现有效工艺路线的优化决策,得到满足车间要求的最优或接近最优的工艺路线。通过实例,验证了该算法在面向STEP-NC加工特征的零件工艺路线优化上的可行性和有效性。

面向STEP-NC基于加工特征规则聚类的零件装夹规划

针对零件工艺规划过程中的装夹规划问题,提出一种面向STEP-NC加工特征的启发式聚类装夹规划求解方法。在分析STEP-NC数据模型和加工单元的基础上,建立了零件装夹规划的数学模型,基于加工特征制造优先级规则对加工单元进行聚类分组,形成零件的装夹方案集;随后通过专家打分和评定策略对这些装夹方案集进行排序,生成符合零件装夹要求的装夹规划方案。装夹规划考虑了零件的具体形状及其稳定性等多种约束条件,确定零件每一个加工单元的定位面和装夹面,较好地体现了零件实际加工过程中的装夹情况。基于Solid-works三维计算机辅助设计平台实现了零件装夹规划的生成,通过实例对所提算法进行了验证。

基于STEP-NC数控铣削最优系统

目的研究STEP-NC数控铣削加工中铣削加工参数的选择,建立基于STEP-NC数控铣削系统的知识库.方法在VC++6.0环境下,基于人工神经网络技术构建知识库,应用改进的BP算法优化铣削加工参数.结果STEP-NC数控铣削知识库依据输入的加工材料、加工特征、刀具信息等条件,快捷且合理地优选出了铣削加工参数,并将数据传输给数控系统中的刀具路径生成器.结论STEP-NC数控铣削知识库具有自适应和自学习的能力,铣削加工参数的优化提高了数控加工质量和加工效率.

基于规则和混合算法的智能STEP-NC微观工艺规划

为解决STEP-NC微观工艺规划智能性差和效率不高的问题,提出一种基于规则和混合算法的智能STEP-NC微观工艺规划方法.首先,采用本文提出的基于规则的加工资源匹配算法,为每一加工工步匹配相应的机床和刀具;然后,以最短换刀时间为优化目标,采用混合算法(混沌算法和遗传算法)对STEP-NC加工工步序列进行优化;最后,以最少加工时间和最低加工成本为优化目标,通过混合算法对STEP-NC加工工艺参数进行优化,进而得到最优的STEP-NC微观工艺规划.通过实例验证了该方法的有效性和可行性.

基于图论和改进Dijkstra算法的STEP-NC复杂型腔最短刀具路径生成方法

针对STEP-NC(standard for the exchange of product data,STEP;STEP-compliant numerical control,STEP-NC)复杂型腔的刀具路径生成问题,本文提出了一种基于图论和改进Dijkstra算法的STEPNC复杂型腔最短刀具路径生成方法.在该方法中,首先根据走刀行距和基本元素的等距偏置,生成STEPNC复杂型腔封闭等距环.然后,基于图论得到封闭等距环的赋权有向图.最后,利用改进的Dijkstra算法生成STEP-NC复杂型腔最短刀具路径.通过实例验证了所提出方法的可行性和有效性.

基于STEP-NC的工艺规划优化方法研究

在重点分析零件特征典型加工方案及加工方案组合优化原则的基础上,基于STEP-NC应用遗传算法建立了零件加工方案组合优化的数学模型。并将该方法应用到具体零件的工艺排序决策过程中,通过编码、杂交、复制、变异等得到满足零件要求的最优或接近最优的工艺路线。验证了遗传算法在STEP-NC工艺路线排序中的有效性。

面向STEP-NC自由曲面特征的加工操作方法智能决策

为提升复杂零件工艺规划的集成化和智能化,提出一种基于混合算法面向STEP-NC自由曲面特征的加工操作方法智能决策方法.首先,构建了面向STEP-NC自由曲面特征的加工操作方法决策BP神经网络模型.然后,基于自适应视野策略、自适应步长策略和混沌算法给出了改进的人工鱼群算法,并与BP神经网络相融合设计了用于STEP-NC自由曲面特征加工操作方法决策的混合算法.并利用归一化的零件加工信息实现了STEP-NC自由曲面特征的加工操作方法高效智能决策.最后,通过实例验证了该方法的有效性和可行性.

遗传算法在STEP-NC工艺规划方面的应用研究

在重点分析零件特征典型加工方案及加工方案组合优化原则的基础上,基于STEP-NC应用遗传算法建立了零件加工工艺优化的数学模型。并将该方法应用到具体零件的工艺排序决策过程中,通过编码、杂交、复制、变异等得到满足零件要求的最优工艺路线。验证了遗传算法在STEP-NC工艺路线优化中的有效性。

RS码改进的step-by-step译码算法

提出一种改进的 RS码的 step- by- step译码算法 ,给出了算法的基本原理 ,以及具体的并行算法与串行算法 ,采用此改进算法可以显著降低计算的复杂度 ,并提高译码速度。

RS码的Step-by-step译码及软件实现

RS码以其强大的纠正随机错误和突发错误的能力,被广泛地应用于各种数字通信系统中。其译码算法是实现RS码的核心,Step-by-step算法与传统的译码算法相比,不用计算错误位置多项式,可以大大地节省译码时间。文章在分析RS码的基本原理及Step-by-step译码算法的基础上,介绍了一种用软件来实现Step-by-step译码算法的方案,并且给出了流程图。

基于两步正则化Gauss-Newton迭代算法的ECT图像重建

电容层析成像(ECT)技术求解图像重建问题属于非线性问题,并且存在严重的不适定性。为提高图像重建精度,提出了一种基于两步正则化Gauss-Newton迭代算法的ECT图像重建方法。针对标准正则化Gauss-Newton迭代算法在图像重建中存在的不收敛问题,引入了两步迭代方法;改进了正则化矩阵,提高了解估计的精确度;考虑到Gauss-Newton算法对迭代初值的依赖性,加入了同伦算法。最后,进行仿真和静态实验,并与线性反投影(LBP)算法、Landweber算法、Tikhonov正则化算法进行对比。结果表明,该方法可有效提高图像重建精度。

基于改进APF-Informed-RRT^(*)的机械臂避障路径规划

针对Informed-RRT^(*)算法在避障路径规划中缺乏目的性和方向性,存在规划时间长、迭代效率低等问题,提出了结合人工势场法和Informed-RRT^(*)算法的避障规划算法。首先,针对传统人工势场法存在目标点不可达、易与障碍物碰撞的问题,提出了改进后的人工势场法,并将其融入Informed-RRT^(*)算法中,使随机树沿势场下降的方向生长,增强其方向性;其次,依据随机树与障碍物间的距离,提出了一种自适应生长步长策略,提高了对空间的探索能力;最后,引入贪心算法的思想,在生长时直接判断随机树能否直达目标点,提高了路径规划效率。在二维和三维环境下对改进后的算法与传统算法及其衍生算法进行对比实验,仿真结果表明改进后的Informed-RRT^(*)算法相较于原始算法规划的路径长度和规划耗时分别减少了17.42%和36.21%。

Motion Planning Method for Obstacle Avoidance of 6-DOF Manipulator Based on Improved A* Algorithm

The conventional A* algorithm may suffer from the infinite loop and a large number of search data in the process of motion planning for manipulator. To solve the problem,an improved A* algorithm is proposed in this paper by the means of selecting middle points and applying variable step segments searching during the searching process. In addition,a new method is proposed for collision detection in the workspace. In this paper,the MOTOMAN MH6 manipulator with 6-DOF is applied for motion plan. The algorithm is based on the basis of the simplification for the manipulator and obstacles by cylinder enveloping. Based on the analysis of collision detection,the free space can be achieved which makes it possible for the entire body to avoid collisions with obstacles. Compared with the Conventional A*,the improved algorithm deals with less searching points and performs more efficiently. The simulation developed in VC + + with OpenGL and the actual system experiments prove effectiveness and feasibility of this improved method.

基于POD-αATS的油浸变压器瞬态温升降阶自适应变步长计算方法

针对油浸式电力变压器瞬态温升计算效率过低的问题,该文提出本征正交分解-αATS(proper orthogonal decomposition-adaptive time stepping based onαfactor,POD-αATS)降阶自适应变步长瞬态计算方法。首先,推导变压器绕组瞬态温升计算的有限元离散方程;其次,采用POD降阶算法改善传统瞬态计算中存在的条件数过大及方程阶数过高的问题;同时对于瞬态计算中的时间步长选择问题,提出适用于非线性问题的αATS变步长策略;然后,为验证方法的有效性,基于110 kV油浸式电力变压器绕组的基本结构建立二维八分区数值计算模型,同时将计算结果与基于110 kV绕组的温升实验结果进行对比。数值计算及实验结果表明,所提算法与全阶定步长算法在流场和温度场中的精度几乎相同,且流场计算效率提升约45倍,温度场计算效率提升约38倍,计算速度得到显著提高。这一点在温升实验中同样得到验证,说明该文所提算法的准确性、高效性及一定的工程实用性。

A Chaos Sparrow Search Algorithm with Logarithmic Spiral and Adaptive Step for Engineering Problems

The sparrow search algorithm(SSA)is a newly proposed meta-heuristic optimization algorithm based on the sparrowforaging principle.Similar to other meta-heuristic algorithms,SSA has problems such as slowconvergence speed and difficulty in jumping out of the local optimum.In order to overcome these shortcomings,a chaotic sparrow search algorithm based on logarithmic spiral strategy and adaptive step strategy(CLSSA)is proposed in this paper.Firstly,in order to balance the exploration and exploitation ability of the algorithm,chaotic mapping is introduced to adjust the main parameters of SSA.Secondly,in order to improve the diversity of the population and enhance the search of the surrounding space,the logarithmic spiral strategy is introduced to improve the sparrow search mechanism.Finally,the adaptive step strategy is introduced to better control the process of algorithm exploitation and exploration.The best chaotic map is determined by different test functions,and the CLSSA with the best chaotic map is applied to solve 23 benchmark functions and 3 classical engineering problems.The simulation results show that the iterative map is the best chaotic map,and CLSSA is efficient and useful for engineering problems,which is better than all comparison algorithms.