基于数学形态滤波器抑制局部放电窄带周期性干扰的研究

变压器局部放电在线检测的关键技术之一是有效抑制现场强烈的电磁干扰。文中引入非线性数学形态学变换，基于最小均方(LMS)算法构造了一种结构元素自适应的形态开、闭组合形态滤波器，很好地解决了局部放电在线检测的窄带周期性干扰问题。仿真表明该方法对窄带周期性干扰的噪声抑制比(NNR)大于60dB，而现场实际测量的噪声抑制比超过20dB。数学形态滤波器为电力变压器局部放电在线检测提供了一种新的干扰抑制方法。     

基于进化算法的压气机叶型多目标优化设计

该文发展了基于进化计算和Navier-Stokes方程求解技术的压气机叶型气动多目标优化设计技术。压气机叶型气动优化设计目标是静压升最大和总压损失最小。文中采用Bezier曲线参数化叶栅几何型线、相应的控制点坐标作为设计变量：压气机叶型气动性能采用Reynolds平均Navier-Stokes方程和Baldwin—Lomax代数紊流模型进行评价；采用实数型多目标进化计算作为优化算法对叶型进行优化设计。优化设计结果得到一组Pareto解集，并且将特定的Pareto解和初始叶型进行详细的气动性能分析比较。优化设计结果证明了该文发展的多目标优化设计技术的有效性和实用性。     

基于均匀设计的多目标自适应遗传算法及应用

提出一种多目标遗传算法,将均匀设计技术应用于适应度函数合成和交叉算子构造,以提高遗传算法的空间搜索均匀性、子代质量和运算效率.分析和实验结果表明,该方法可缩短算法运行时间和得到分布较均匀的Pareto有效解集;配合基于元件标称值的网表级高效编码方案和考虑基因位差异的遗传概率调整策略,可实现模拟电路自动设计,通过单次运行即获得对应不同偏好的多种实用化设计结果.     

A fast boundary cloud method for 3D exterior electrostatic analysis

An accelerated boundary cloud method (BCM) for boundary‐only analysis of 3D electrostatic problems is presented here. BCM uses scattered points unlike the classical boundary element method (BEM) which uses boundary elements to discretize the surface of the conductors. BCM combines the weighted least‐squares approach for the construction of approximation functions with a boundary integral formulation for the governing equations. A linear base interpolating polynomial that can vary from cloud to cloud is employed. The boundary integrals are computed by using a cell structure and different schemes have been used to evaluate the weakly singular and non‐singular integrals. A singular value decomposition (SVD) based acceleration technique is employed to solve the dense linear system of equations arising in BCM. The performance of BCM is compared with BEM for several 3D examples. Copyright © 2004 John Wiley & Sons, Ltd.     

A new unified theory underlying time dependent linear first‐order systems: a prelude to algorithms by design

A new unified theory underlying the theoretical design of linear computational algorithms in the context of time dependent first‐order systems is presented. Providing for the first time new perspectives and fresh ideas, and unlike various formulations existing in the literature, the present unified theory involves the following considerations: (i) it leads to new avenues for designing new computational algorithms to foster the notion of algorithms by design and recovering existing algorithms in the literature, (ii) describes a theory for the evolution of time operators via a unified mathematical framework, and (iii) places into context and explains/contrasts future new developments including existing designs and the various relationships among the different classes of algorithms in the literature such as linear multi‐step methods, sub‐stepping methods, Runge–Kutta type methods, higher‐order time accurate methods, etc. Subsequently, it provides design criteria and guidelines for contrasting and evaluating time dependent computational algorithms. The linear computational algorithms in the context of first‐order systems are classified as distinctly pertaining to Type 1, Type 2, and Type 3 classifications of time discretized operators. Such a distinct classification, provides for the first time, new avenues for designing new computational algorithms not existing in the literature and recovering existing algorithms of arbitrary order of time accuracy including an overall assessment of their stability and other algorithmic attributes. Consequently, it enables the evaluation and provides the relationships of computational algorithms for time dependent problems via a standardized measure based on computational effort and memory usage in terms of the resulting number of equation systems and the corresponding number of system solves. A generalized stability and accuracy limitation barrier theorem underlies the generic designs of computational algorithms with arbitrary order of accuracy and establishes guidelines which cannot be circumvented. In summary, unlike the traditional approaches and classical school of thought customarily employed in the theoretical development of computational algorithms, the unified theory underlying time dependent first‐order systems serves as a viable avenue to foster the notion of algorithms by design. Copyright © 2004 John Wiley & Sons, Ltd.     

Numerical solution of second‐order,two‐point boundary value problems using continuous genetic algorithms

Second‐order, two‐point boundary‐value problems are encountered in many engineering applications including the study of beam deflections, heat flow, and various dynamic systems. Two classical numerical techniques are widely used in the engineering community for the solution of such problems; the shooting method and finite difference method. These methods are suited for linear problems. However, when solving the non‐linear problems, these methods require some major modifications that include the use of some root‐finding technique. Furthermore, they require the use of other basic numerical techniques in order to obtain the solution. In this paper, the author introduces a novel method based on continuous genetic algorithms for numerically approximating a solution to this problem. The new method has the following characteristics; first, it does not require any modification while switching from the linear to the non‐linear case; as a result, it is of versatile nature. Second, this approach does not resort to more advanced mathematical tools and is thus easily accepted in the engineering application field. Third, the proposed methodology has an implicit parallel nature which points to its implementation on parallel machines. However, being a variant of the finite difference scheme with truncation error of the order O(h²), the method provides solutions with moderate accuracy. Numerical examples presented in the paper illustrate the applicability and generality of the proposed method. Copyright © 2004 John Wiley & Sons, Ltd.     

A changing range genetic algorithm

During the last decade various methods have been proposed to handle linear and non‐linear constraints by using genetic algorithms to solve problems of numerical optimization. The key to success lies in focusing the search space towards a feasible region where a global optimum is located. This study investigates an approach that adaptively shifts and shrinks the size of the search space to the feasible region; it uses two strategies for estimating a point of attraction. Several test cases demonstrate the ability of this approach to reach effectively and accurately the global optimum with a low resolution of the binary representation scheme and without additional computational efforts. Copyright © 2004 John Wiley & Sons, Ltd.     

基于变异概率自适应调整的逆序遗传算法研究

提出一种改进的遗传算法，根据个体适应度不同对变异概率进行自适应调整，使群体中的优良模式不易被破坏，同时又保证了种群个体的多样性，从而提高了算法的搜索效率。算法中改变了交叉与变异的操作顺序，避免了个体适应度的重复计算，提高运行速度。仿真结果表明，该算法优于普通遗传算法。     

遗传算法参数寻优的分形插值方法研究

分形插值是一种构造分形曲线的方法。分形插值方法中的垂直比例因子的大小影响拟合函数曲线的起伏，控制插值曲线的拟合精度。该文在深人分析分形插值方法的基础上，结合遗传算法可以有导向的实现参数随机搜索优化的特点，将其应用到分形插值自由参数的选取中，实现分形插值曲线与实际函数的最佳拟合。仿真结果验证了遗传算法在垂直比例因子的寻优上的有效性。     

遗传算法的收敛性分析

理论分析算法发生过早收敛的原因、收敛速度与各个控制参数以及适应度函数的关系,并提出与证明了判断遗传算法收敛性的统一判据。