复杂系统可靠度最优分配问题是一个具有多局部极值的非线性的优化问题。
An optimal apportionment of reliability problem of complex system is a nonlinear optimization problem with a large number of local extreme values.
实验表明，模糊互信息函数比互信息函数具有更少的局部极值点和更好的优化特性。
Experiment results show that similarity measure based on fuzzy mutual information has less local extremums and better optimization character.
微粒群优化算法是求解连续函数极值的一个有效方法。
Particle Swarm Optimization (PSO) algorithm is a powerful method to find the extremum of a continuous numerical function.
为了避免神经网络的学习过程陷入局部极值点，采用人工免疫网络优化神经网络的参数。
In order to prevent neural network learning from getting into local extreme point, artificial immune network algorithm was used to optimize neural network's parameters.
多峰值函数的极值问题一直是优化领域中的一个难点和热点。
Extremum problem of multimodal functions is a difficult issue in optimization fields.
经典的粒子群优化算法是一个在连续的定义域内搜索数值函数极值的很有效的方法。
The classical Particle swarm optimization (PSO) algorithm is a powerful method to find the minimum of a numerical function, on a continuous definition domain.
短期水电优化是过程问题，应该用泛函极值的变分法即欧拉方程求解。
Short term optimization of hydroelectric generation is a procedure problem and should be solved by the variational method of functional extreme value, i. e. , the famous Eulerian equation.
提出数控机床热误差鲁棒建模的综合极值法和优化试验设计法两种新方法。
In this paper, two new methods of thermal error robust modeling for NC machine tools are proposed: one is the comprehensive extreme value method, the other is the optimal experiment design method.
该方法通过在遥感图像上选取局部极值点来构成平面散乱数据点集，并在此基础上进行三角剖分、优化和三角插值曲面构造。
It gets the plane scattered data points through selecting the local extremum points on the remote image, and then triangulates, constructs the triangular interpolating surface on it.
对于变分同化中经常遇到的多极值问题，一般的优化算法无法解决。
The ordinary optimization algorithm can not solve the multi-extreme value problem in data assimilation, so an improvement to steepest descent algorithm is proposed to solve the problem.
针对多极值连续函数优化问题，提出了一种自适应蚁群算法。
An adaptive ant colony algorithm is presented for the optimization of multi-minimum continuous function.
动态规划法是运筹学中的一种常用的优化算法，可以用来求解约束条件下的函数极值问题。
Dynamic programming is an optimal arithmetic which is commonly used in operational research and can be used to solve the extreme value of the function in restricted condition.
与以往方法不同，本文将立体匹配问题看作一种多极值的优化问题——从一组可能的视差图中找到最合适的一个。
Different from previous methods, this approach casts the stereo matching as a multi-extrema optimization problem such that finding the fittest solution from a set of potential disparity maps.
然而，结构优化问题相当困难，因为其势能曲面上局部极值的数量非常多而且随着体系尺寸呈指数增长。
However, the structural optimization problem is notoriously difficult because the number of local minima tends to grow exponentially with system size.
许多经济系统中的优化问题，可化为用单值非线性算子或多值算子形成约束条件的条件极值问题。
All know that most of the optimization of economic system can be converted into conditional extreme value problem which takes nonlinear operator or multi operator as constraints.
并利用混沌优化的方法一次寻找出图像熵的多个极值点，提高了阈值寻找的效率。
Chaos optimization is used to search all local optimal points of the entropy function in once searching, which can improve the efficiency of searching threshold.
本文为中低速内燃机配气机构的最佳设计，提供了统一的优化模型——条件泛函极值问题。
In this paper, we present a unified optimal model, i. e., the minimizing functional with constraints for the optimal design of critical and low velocity internal combustion engine valve mechanism.
测量上有许多极值问题，如各种准则下的平差，权的最优分配，网的优化方案等，多可归于线性极值问题，且多不适用寻常数学解法。
There are many extreme problems in surveying, like the optimal distribution of weight, optimal design of networks, the method of least absolute sum etc.
电网故障诊断优化模型可将诊断问题表示成求极值的0-1整数规划问题，从而可通过严密的数学运算来确知故障设备。
A model for optimizing fault diagnosis for power systems is formulated as a 0-1 integer programming problem. The fault equipment can be determined by means of refined mathematical manipulation.
复杂地形，特别是山地条件下地震勘探中的大静校正问题是一个非线性的、具有多参数多极值的全局优化难题。
For the areas of complex topography, especially mountain area, static is a non-linear hybrid optimization problem with the presence of multi-parameters and multi-extremes.
针对粒子群优化算法(PSO)应用于多极值点函数易陷入局部极小值，提出旋转曲面变换(RST)方法。
Aimed at particle swarm optimization (PSO) algorithm being easily trapped into local minima value in multimodal function, a rotating surface transformation (RST) method was proposed.
粒子群优化算法应用于多极值点函数优化时，存在陷入局部极小点和搜寻效率低的问题。
Particle Swarm optimization (PSO) algorithm is a population-based global optimization algorithm, but it is easy to be trapped into local minima in optimizing multimodal function.
针对K均值聚类算法依赖于初始值的选择，且容易收敛于局部极值的缺点，提出一种基于粒群优化的K均值算法。
Local optimality and initialization dependence disadvantages of K-means are analyzed and a PSO-based K-means algorithm is proposed.
由于较粗尺度下目标函数呈现较强的凸性及较少的局部极值，很有利于收敛至优化解。
Objective function presents comparatively strong convexity and lesser local extreme values on wide scale which is favour of converging to optimized values.
实验实例表明：该算法收敛速度快，有极强的避免过早收敛及避免局部极值的全局优化的能力。
The position and the weighted coefficients can be optimized at the same time. The cases showed that this method had a strong ability to find to global optimization solution.
该算法基本保持了粒子群优化算法简单容易实现的特点，但改善了粒子群优化算法摆脱局部极值点的能力，提高了算法的收敛速度和精度。
The proposed algorithm is almost as simple for implement as particle swarm optimizer, but can improve the abilities of seeking the global excellent result and evolution speed.
粒子群优化聚类算法具有参数简单，收敛快等优势，但也有局部极值问题。
PSO clustering algorithm is known to have simple parameters and fast convergence, but there are also local optimal problems.
粒子群优化聚类算法具有参数简单，收敛快等优势，但也有局部极值问题。
PSO clustering algorithm is known to have simple parameters and fast convergence, but there are also local optimal problems.
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