DNA计算模型在圆柱度误差评定中的应用

提出了DNA计算模型应用于圆柱度误差评定的方法，除了常规遗传算法搜索效率高、全局优化性能好的特点外，该方法的一个突出特点是可以根据待求设计参数的取值范围，设计相应的参数遗传密码表，从而将所有的误差评定算法统一在一个算法之中。     

线性切比雪夫逼近理论评定形状误差的改进算法

“最小条件”是评定形状误差的基本原则,切比雪夫逼近理论评定形状误差满足“最小条件”,其中以“小偏差”假设为基础的评定算法最为经典.但当评定对象不满足“小偏差”假设时,该算法将无法保证评定结果的精确性.本文提出了一种改进算法,该算法不受“小偏差”假设的约束,通过实例验证了该算法的可行性和适用性.     

粒子群优化算法及其在圆度误差评定中的应用

提出一种基于粒子群优化算法(PSO)的圆度误差评定方法。介绍了PSO算法的提出及其特点;具体阐述了PSO算法的基本原理和实现步骤;提出圆度误差评定这一非线性优化问题,给出其优化目标函数及PSO算法的适应度函数和编码方式;结合实例对算法参数进行了设置,通过实例运算对PSO进行了正确性和精确性验算。实例证明该方法能够很好地解决圆度误差评定问题,与遗传算法具有相当的计算精度,能够获得精度较高的结果。而PSO的突出优点是简单易于实现,计算速度快。     

基于模糊神经网络的设备故障预测研究

对模糊神经网络技术进行了研究，提出了预测分析的模糊神经网络模型；建立了故障指标评定方法，利用预测算法运用参数历史故障指标对参数指标进行趋势预测，预测得到的参数指标可以根据专家诊断系统判据进行诊断，对未来设备的健康状况进行可信度较高的评估。经仿真结果验证，该算法预测精度较高，预测结果可信.     

圆锥体全局角度尺寸评定方法

提出了基于圆周轮廓提取方案的圆锥全局角度尺寸的评定方法,建立了圆锥最小二乘全局角度尺寸和最大最小全局角度尺寸的评定模型。基于所建立的评定模型,给出了圆锥全局角度尺寸评定的飞蛾扑火优化算法(MFO)的流程图,并编制了圆锥全局角度尺寸评定及最小二乘圆锥面、最大最小圆锥面可视化程序。采用所编制的程序,对4个圆锥仿真试样进行了最小二乘全局角度尺寸和最大最小全局角度尺寸评定,对其不同评定方法、不同优化初始参数和不同优化方法得到的全局评定结果进行了比较。用乌鸦搜索算法、人工生态优化算法、平衡优化器算法和粒子群优化算法对1个试样的最大最小全局角度尺寸进行了评定,并与MFO的评定结果进行了比较。除了优化初始值对评定结果有影响外,搜索区间、种群数和最大迭代次数对评定结果均有影响,但其规律性不强。总体上讲,MFO的评定结果优于其他4种优化方法的评定结果。     

基于人工免疫算法的轴线直线度误差评定

提出了一种满足最小区域条件的轴线直线度误差评定方法--人工免疫优化算法.介绍了该算法的原理, 根据最小条件建立了轴线直线度误差评定的数学模型及优化目标函数, 给出了该算法的具体实现方法.实例计算结果表明,人工免疫优化算法评定精度优于最小二乘法,能够全局寻优,而且易于实现.     

基于残差一次累加生成的不确定度灰评定方法

介绍了一种改进的基于残余误差一次累加生成的A类不确定度灰评定方法。首先对测量数据序列进行排序;然后求排序后各测量数据的残余误差,对残余误差作一次累加生成计算,得到残余误差一次累加序列;最后,在残余误差一次累加序列中查找绝对值最大者,将其作为A类不确定度灰评定的参数。分析结果表明,改进的基于残余误差一次累加生成灰评定方法的算法复杂性明显低于基于原始数据一次累加生成算法的复杂性,且采用该灰评定方法得到的A类不确定度结果与采用经无偏修正的贝塞尔公式计算得到的A类不确定度结果有相当高的一致性。     

微分进化算法在圆度误差评定中的应用

为了精确快速计算圆度误差,提出了基于微分进化智能优化算法的最小区域圆度误差评定方法。介绍了微分进化算法的基本原理及种群初始化、变异、交叉、选择实现步骤,建立了该算法求解最小区域圆度误差的数学模型。为验证算法的有效性,进行了大量实验并与多种算法进行对比,证实了方法的评定结果不仅小于最小二乘法及标准遗传算法评定结果,精度高,而且计算结果稳定,运算速度快。实验表明:微分进化算法用于最小区域圆度误差评定有较强的自适应能力、快速全局收敛性和高稳定性,适于对高精度圆度误差的快速评定。     

基于改进置换算法的圆参数评定

提出了按最小包容区域法评定圆度误差的改进置换算法,利用拟合精度较高的相对代数距离法设置置换算法的起点,符合最小条件,减少迭代次数,加快计算速度,提高拟合精度.建立了圆参数评定的数学模型,设计了相应的误差评定软件,成功地应用到了微机型万能工具显微镜的测量软件上,并给出一个影像法测量光滑环规直径和圆度误差的实例,将改进置换算法的评定结果与其它评定方法进行了比较.结果表明,改进置换算法具有较高的拟合精度和计算速度.     

按最小区域法评定圆度误差的优化算法

文章提出符合圆度误差的评定准则的枚举盲目算法,然后对算法优化,分析用分治策略法和优先搜索法减低算法的时间复杂度和搜索效率的方法,经测试证明该算法有较快的运行速度。     

二维精密工作台离散点测量结果误差分离的新方法研究

针对测量结果中包含的误差,提出一种能够分离精密工作台系统误差的方法。首先利用辅助测量栅格板和二维精密工作台的不同位置进行测量,然后根据栅格板上标记点的测量数据和标称值建立误差分离的数学模型,最终实现对测量结果的误差分离。通过仿真验证了算法的有效性。仿真结果表明:当不存在测量噪声时,能够实现误差的完全分离;当存在测量噪声时,计算值与给定值标准差的相对误差在X轴和Y轴上分别为1.95%和1.52%。对于不同幅度的噪声,工作台系统误差计算值稳定。该算法对噪声不敏感,表现出很好的鲁棒性,可用于测量结果和仪器性能的评价。     

A new colour constancy algorithm based on automatic determination of gray framework parameters using neural network

Colour constancy is defined as the ability to estimate the actual colours of objects in an acquired image disregarding the colour of scene illuminant. Despite large variety of existing methods, no colour constancy algorithm can be considered as universal. Among the methods, the gray framework is one of the best-known and most used approaches. This framework has some parameters that should be set with appropriate values to achieve the best performance for each image. In this article, we propose a neural network-based algorithm that aims to automatically determine the best value of gray framework parameters for each image. It is a multi-level approach that estimates the optimal values for the gray framework parameters based on relevant features extracted from the input image. Experimental results on two popular colour constancy datasets show an acceptable improvement over state-of-the-art methods.     

Study of validity of the single-diode model for solar cells by I–V curves parameters extraction using a simple numerical method

The I–V measurement of solar cells is one of the most employed electrical characterization techniques in the photovoltaics research field due to the valuable information one can obtain from such a curve. Parameters like Voc, Isc and the maximum power can be easily observed at a glance. Furthermore, additional information can be extracted by a more exhaustive analysis involving the equivalent electrical circuit of a solar cell which is based on ideal electrical components with a well-defined behavior. This equivalent circuit is typically assumed to be formed by a current source in parallel with a single diode and a shunt resistor, connected to a series resistor. However, this model does not take into account the separate contributions of the different carrier transport mechanisms in solar cells; for example, carrier diffusion and recombination-generation in the depletion region. Therefore, the single diode model may not be suitable in many practical cases. In this work, a simple numerical method was developed to extract the parameters for both single diode and double diode models from experimental I–V curves of solar cells. The developed numerical algorithm was applied to extract the parameters for a published benchmark solar cell which has been used for testing this kind of algorithms. The extracted parameters using our simple method are comparable with other more sophisticated and computer power demanding algorithms. Thereafter, we applied the developed algorithm to extract the single-diode parameters from simulated “experimental” I–V curves, where two carrier transport mechanisms are present, trying to understand under what conditions the single diode approximation would provide meaningful parameters for such experimental curves. It is shown that the extracted parameters can vary strongly, particularly for the dark saturation current and ideality factor, without much variation of the root mean square error between the experimental data and the model, causing these values to be unreliable and its physical interpretation misleading. We show that the same algorithm can be applied to a double diode (two exponentials) model providing physically meaningful parameters without much computing power requirements. In summary, there is no further justification for using a single diode model to interpret the experimental I–V curves of real solar cells.     

Optimal multichannel estimation of the location of a target with nonoverlapping noise

We propose a new algorithm for estimating the location of an object in multichannel images when the noise is spatially disjointed from (nonoverlapping with) the target. This algorithm is optimal for nonoverlapping noise and for multichannel images in the maximum-likelihood sense. We consider the case in which the statistical parameters of the input scene are unknown and are estimated by observation. We assess the results for simulated images with white and Gaussian background, for a large scale of variances of the background noise, and different values of the contrast in the scene. We compare the results of this algorithm with the results obtained with two other algorithms, the optimal algorithm for monochannel nonoverlapping noise and the optimal algorithm for multichannel additive noise, and we show that in both cases improvement can be obtained. We show the efficiency of the estimation for real input scenes when the background noise is correlated clutter noise. This algorithm has the same complexity as correlation, and the improvement is obtained with no more calculation cost than with classic methods.     

Two new decoding algorithms for Reed-Solomon codes

The subject of decoding Reed-Solomon codes is considered. By reformulating the Berlekamp and Welch key equation and introducing new versions of this key equation, two new decoding algorithms for Reed-Solomon codes will be presented. The two new decoding algorithms are significant for three reasons. Firstly the new equations and algorithms represent a novel approach to the extensively researched problem of decoding Reed-Solomon codes. Secondly the algorithms have algorithmic and implementation complexity comparable to existing decoding algorithms, and as such present a viable solution for decoding Reed-Solomon codes. Thirdly the new ideas presented suggest a direction for future research. The first algorithm uses the extended Euclidean algorithm and is very efficient for a systolic VLSI implementation. The second decoding algorithm presented is similar in nature to the original decoding algorithm of Peterson except that the syndromes do not need to be computed and the remainders are used directly. It has a regular structure and will be efficient for implementation only for correcting a small number of errors. A systolic design for computing the Lagrange interpolation of a polynomial, which is needed for the first decoding algorithm, is also presented.This research was supported by a grant from the Canadian Institute for Telecommunications Research under the NCE program of the Government of Canada     

参数化CAD中参数有效范围的算法研究

为了解决在参数化CAD系统中由于参数赋值不合理而导致的几何实体重建失败的问题,提出了确定一类二维参数化CAD模型中参数有效范围的代数算法。该算法可以实时求解所有简单多边形中距离约束参数的有效取值范围,并利用几何变换简化求解规模,提高求解效率。研究结果表明,该算法提供的有效取值范围内的任一赋值,均可保证重建后几何实体的拓扑形状不发生改变,在一定程度上提高了参数化CAD软件的设计效率和人机交互的智能化水平。同时对算法复杂度进行了分析,该算法的复杂度为O(n~2)。     

Estimating time-series models from irregularly spaced data

Maximum-likelihood estimation of the parameters of a continuous-time model for irregularly sampled data is very sensitive to initial conditions. Simulations may converge to a good solution if the true parameters are used as starting values for the nonlinear search of the minimum of the negative log likelihood. From realizable starting values, the convergence to a continuous-time model with an accurate spectrum is rare if more than three parameters have to be estimated. A discrete-time spectral estimator that applies a new algorithm for automatic equidistant missing-data analysis to irregularly spaced data is introduced. This requires equidistant resampling of the data. A slotted nearest neighbor (NN) resampling method replaces a true irregular observation time instant by the nearest equidistant resampling time point if and only if the distance to the true time is within half the slot width. It will be shown that this new resampling algorithm with the slotting principle has favorable properties over existing schemes such as NN resampling. A further improvement is obtained by using a slot width that is only a fraction of the resampling time.     

A fuzzy-controlled Hooke-Jeeves optimization algorithm

This article presents an approach to enhance the Hooke-Jeeves optimization algorithm through the use of fuzzy logic. The Hooke-Jeeves algorithm, similar to many other optimization algorithms, uses predetermined fixed parameters. These parameters do not depend on the objective function values in the current search region. In the proposed algorithm, several fuzzy logic controllers are integrated at the various stages of the algorithm to create a new optimization algorithm: Fuzzy-Controlled Hooke-Jeeves algorithm. The results of this work show that incorporating fuzzy logic in the Hooke-Jeeves algorithm can improve the ability of the algorithm to reach an extremum in different typical optimization test cases and design problems. Sensitivity analysis of the variables of the algorithm is also considered.     

Application of a genetic algorithm: near optimal estimation of the rate and equilibrium constants of complex reaction mechanisms

In iterative non-linear least-squares fitting, the reliable estimation of initial parameters that lead to convergence to the global optimum can be difficult. Irrespective of the algorithm used, poor parameter estimates can lead to abortive divergence if initial guesses are far from the true values or in rare cases convergence to a local optimum. For determination of the parameters of complex reaction mechanisms, where often little is known about what value these parameters should take, the task of determining good initial estimates can be time consuming and unreliable. In this contribution, the methodology of applying a genetic algorithm (GA) to the task of determining initial parameter estimates that lie near the global optimum is explained. A generalised genetic algorithm was implemented according to the methodology and the results of its application are also given. The parameter estimates obtained were then used as the starting parameters for a gradient search method, which quickly converged to the global optimum. The genetic algorithm was successfully applied to both simulated kinetic measurements where the reaction mechanism contained one equilibrium constant and two rate constants to be fitted, and to kinetic measurements of the complexation of Cu²⁺ by 1,4,8,11-tetraazacyclotetradecane where two equilibrium and two rate constants were fitted. The implementation of the algorithm is such that it can be generally applied to any reaction mechanism that can be expressed by standard chemistry notation. The control parameters of the algorithm can be varied through a simple user interface to account for parameter range and the number of parameters involved.     

Batch process schedule optimization under parameter volatility

A general strategy to treat the uncertainties in parameters of batch process scheduling has been developed. The strategy consists of three algorithms: flexible planning, flexible scheduling and reactive schedule adaptation. In this paper, we introduce the flexible scheduling and the reactive schedule adaptation algorithms. The flexible scheduling algorithm is based on both a Monte Carlo simulation and a simulated annealing. It can deal with multiple uncertain parameters and any type of probability density function for the uncertain parameter. We seek the flexible schedule that maximizes the expected profit, including net present values of products less raw material and processing costs, as well as due date penalties, inventory costs and setup costs. In reality, the values of uncertain parameters always change after the batch process schedule and plan are set up. Since the flexible schedule has periods of free time that can be used to accommodate uncertainties during the actual production, the reactive schedule adaptation algorithm can modify the flexible schedule in response to any change in an uncertain parameter with little or no penalty. This algorithm finds a new optimal or suboptimal solution under the new condition by using a combination of different local search methods, which are described.