At the initialization stage of algorithm, the location of the particle is initialized by chaos.
在算法的初始化阶段，对粒子的位置混沌初始化；
The basic principle of CPSO algorithm is that chaos initialization is adopted to improve individual quality and chaos perturbation is utilized to avoid the search being trapped in local optimum.
该算法的思想是采用混沌初始化进行改善个体质量和利用混沌扰动避免搜索过程陷入局部极值。
It is also indicated that current WNN has a poor convergence performance because of adopting the random initialization method and gradient training algorithm of traditional BP NET.
还指出由于当前的连续小波神经网络主要使用传统BP神经网络的随机初始化方法和基于梯度的训练算法，因此存在收敛性差的缺点。
Local optimality and initialization dependence disadvantages of K-means are analyzed and a PSO-based K-means algorithm is proposed.
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。
According to the features of passive location systems, a new synthesis track initialization algorithm is presented based on passive location systems.
针对无源定位系统的特点，提出了一种基于无源定位系统的航迹综合起始算法。
In cluster analysis, Fuzzy K-Means (FKM) algorithm is one of the most widely used methods. However, FKM algorithm is much more sensitive to the initialization, and easy to fall into local optimum.
在聚类分析中，模糊k均值算法是目前应用最为广泛的方法之一，然而该算法对初始化敏感，容易陷入局部极值点。
In cluster analysis, Fuzzy K-Means (FKM) algorithm is one of the most widely used methods. However, FKM algorithm is much more sensitive to the initialization, and easy to fall into local optimum.
在聚类分析中，模糊k均值算法是目前应用最为广泛的方法之一，然而该算法对初始化敏感，容易陷入局部极值点。
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