Local minima of this energy then correspond to desired image properties.
该能量的局部极小值是与期望得到的图像属性相关的。
In most cases, these circuit structures may avoid some local minima and find a global minimum.
许多情况下，该电路可以避免一些局部最小值，而找到一个全局最小值。
Due to the localized nature, RBFN can be trained quickly and can avoid falling into local minima.
由于其局部性质，RBFN能被快速训练，避免陷入局部最小。
However, BP network with gradient descent has some defects such as low convergence speed, fall in local minima.
然而基于梯度下降的BP网络存在收敛速度慢、易陷入局部极小的缺陷。
These three local minima differ by virtue of whether the resulting vacuum energy is positive, negative or zero.
我们可以使用真空能量是正、负或零，来区分这三个局部最小值。
It may be possible to overcome local minima and converge more fast when it is used with the initializing method.
若结合本文的初始化方法一同使用，则收敛更快速，且有可能克服局部极小解问题。
Aim To study the standard BP algorithms local minima and learning speed problems and propose the scheme for improvement.
目的对BP学习算法中存在的大量局部极小点以及收敛速度慢问题进行研究并提出相应的改进方案。
Having elevations that always increase away from the coast means that there's no local minima that complicate river generation.
其海拔总是和海岸的距离相反，意味着不会有局部的小且复杂的河流产生。
As a new machine learning method, SVM can solve the small sample, nonlinear, high dimension and local minima, the actual problem.
作为一种新的机器学习方法，SV M能较好地解决小样本、非线性、高维数和局部极小点等实际问题。
SVM solves practical problems such as small samples, nonlinearity, local minima, which exist in most of learning methods, and has a bright future.
支持向量机方法较好地解决了许多学习方法面临的小样本、非线性和局部极小点等问题，具有很好的应用前景。
Essentially, optimal reactive power dispatch is the multi-restraint overall optimization problem with plentiful local minima and discrete variables.
从本质上讲，无功优化调度问题是具有大量的局部极小值的多约束全局优化问题，且含有大量的离散变量。
However, the structural optimization problem is notoriously difficult because the number of local minima tends to grow exponentially with system size.
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。
Numerical simulation results show that, compared with QDPSO, it is effective, with strong ability to avoid being trapped in local minima and robust to initial value.
数值实验结果表明，与量子粒子群优化算法相比，该算法效率高、优化性能好，具有较强的避免局部极小能力，对初值具有较强的鲁棒性。
The method solves the problem of local minima of ICP algorithm, and the described method is also useful in the field of comparative analysis for precision measurement.
采用两步法配准克服了标准ICP算法难以解决的局部最小问题，本算法也可广泛应用于精密测量时测量结果的比较分析。
Experiments show that EPFCM algorithm can gain best cluster centers and optimal cluster structures, and the probability of falling into local minima is greatly reduced.
实验表明，EPFCM算法可以有效地得到最佳的类中心个数，聚类结果不受初始类中心影响，并且陷入局部极小的概率较FCM算法大大降低。
For complex functions with high dimensions, canonical optimization methods are easy to be trapped in local minima and simple random search methods are slow on convergence.
对于高维复杂函数，传统的确定性算法易陷入局部最小，而单一的全局随机搜索算法收敛速度慢。
It is more suitable than simulated annealing and genetic algorithms for solving the problem that presents many local minima. Experimental results show the method is efficient.
本算法比遗传算法和模拟退火算法更适合于解决具有很多局部最小值的冗余传感器系统费用优化的问题，仿真实验结果表明本算法是很有效的。
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.
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。
Radial basis function (RBF) network have unique advantages in control applications due to its features of simple topological structure, quick convergence speed and no local minima.
径向基函数(RBF)神经网络由于其结构简单、收敛速度快、无局部极小等特点使其在控制中的应用有着独特的优势。
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.
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。
The experimental results show that the algorithm can avoid the misclassification of FCM such as dead center, center overlapping and local minima, and accelerate the segmentation speed.
实验结果表明，该算法可以避免FCM的误分类，诸如陷于中心死区、中心重叠和局部极小值，而且提高了分割速度。
Note that local minima and maxima are normal and expected because the free space only increases during a GC cycle and correspondingly decreases when the application is active and allocating.
注意，容易实现本地的最小和最大空间，因为自由空间仅在GC循环期间增加并且在应用程序处于活动时和进行分配时相应减少。
It is confirmed that PSO could overcome intrinsic shortcomings of BP neural network, including low learning efficiency, slow convergence rate, being easy to fall into local minima, etc.
经验证(PSO)优化算法可以有效地克服BP神经网络存在的学习效率低，收敛速度慢以及容易陷入局部极小点等固有缺点。
However, the design for codebook yet strongly depends on the selection of the initial codebook, and it can easily be trapped in local minima, they seem to be much slow and little robust.
但目前的码本设计算法仍存在局部最佳、强烈依赖于初始码本的选取、鲁棒性较差和算法收敛较慢等问题。
Simulation results showed that such an algorithm, which is robust with initial states, can avoid getting stuck in local minima and has better convergence property as well as time property.
仿真结果表明算法对于初始值是稳健的，并且具有很强的克服陷入局部极小能力。
Traditional particle swarm optimization(PSO) algorithms often trap into local minima easily when used for the optimization of high-dimensional complex functions with a lot of local minima.
针对粒子群算法用于高维数、多局部极值点的复杂函数寻优时易陷入局部最优解现象，提出一种改进的带扰动项粒子群算法并进行收敛性分析。
The algorithm can get global minimum easily with a wide variety of functions of hidden neurons, and no problems such as local minima and slow rate of convergence are suffered like BP algorithm.
新算法选择很广一类的隐层神经元函数，可以直接求得全局最小点，不存在BP算法的局部极小、收敛速度慢等问题。
The algorithm can get global minimum easily with a wide variety of functions of hidden neurons, and no problems such as local minima and slow rate of convergence are suffered like BP algorithm.
新算法选择很广一类的隐层神经元函数，可以直接求得全局最小点，不存在BP算法的局部极小、收敛速度慢等问题。
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