Firstly the thesis introduce several improved PSO algorithm and standard PSO which are from basic PSO.
本论文首先介绍了经过基本粒子群算法改进而来的标准粒子群算法以及改进的其他PSO算法。
Guaranteed by the indentation factor, the improved PSO algorithm can keep the iteration particles in feasible region.
通过引入缩进因子，改进P SO算法，使粒子在迭代过程中保持在可行域内。
The simulation results show that the improved PSO algorithm can solve the high-dimensional numerical optimization problem effectively.
实验结果表明该改进微粒群算法可以有效地解决高维数值优化问题。
For improving the predicting results, two improved PSO algorithm are presented also in this paper: Velocity Mutation PSO and hybrid PSO.
在此基础上，进一步提出了混合粒子群算法和速度变异粒子群算法两种改进算法提高优化性能。
An improved particle swarm optimization (PSO) algorithm was designed. And a weighted ITAE index of turbine speed error was taken as the fitness function of the improved PSO algorithm.
提出了一种新的改进的粒子群优化算法，并以水轮机转速偏差的加权ITAE指标作为改进粒子群优化算法的适应度函数。
It USES the dynamic scale-free like network as the particle's optimization neighborhood. It proposes an improved PSO algorithm based on variety inertia weight and dynamic neighborhood.
将有向动态类无标度网作为粒子寻优邻域，提出一种基于变惯性权重及动态邻域的改进P SO算法。
The improved PSO algorithm is tested in two models, to show the effects of improvement and its ability in solving the inverse time overcurrent relay coordination problem when DG units are connected.
通过对两个算例的分析可看到改进算法的效果以及粒子群算法解决分布式电源并网情况下反时限过流保护整定问题的能力。
Building up decision tree by improved PSO, the paper gives the example to validate that the improved algorithm is better than the original decision tree method and by improved by GA.
将改进的P SO引入到决策树建树方法中，并与传统的决策树方法及使用遗传算法改进后的树进行比较，验证了其优越性。
The parameters and thresholds of classifiers are optimized by improved Particle Swarm Optimization(PSO) algorithm.
改进的粒子群优化算法全局搜索BP神经网络的权值和阈值。
And, in FNN weight training, improved PSO in the convergence rate and the ability to jump out to local optimum algorithm is better than BP.
且改进的粒子群算法在模糊神经网络权值的训练中收敛速度和跳出局部最优的能力都要比BP算法更优。
The proposed model is solved by improved particle swarm optimization (PSO) algorithm.
针对此模型，采用改进粒子群优化算法进行求解。
The test results on benchmark functions show that ADPSO achieves better solutions than other improved PSO, and it is an effective algorithm to solve multi-objective problems.
在基准函数的测试中，结果显示ADPSO算法比其他PSO算法有更好的运行效果，是求解多峰问题的一种有效算法。
Experimental results show that the improved algorithm performs better than the traditional PSO and may avoid falling into the local optimum instead.
实验结果证明，与传统PSO算法相比，改进算法的寻优效果较好，可在一定程度上避免陷入局部最优。
Second, an Enhanced Phase Locked Loop (EPLL) control strategy based on improved Adaptive Notch Filtering (ANF) is proposed, controller parameters are optimized using BF-PSO algorithm.
其次，提出了一种基于改进型ANF的三相EPLL控制策略，并用BF-PSO算法对控制器参数进行优化设计。
Second, an Enhanced Phase Locked Loop (EPLL) control strategy based on improved Adaptive Notch Filtering (ANF) is proposed, controller parameters are optimized using BF-PSO algorithm.
其次，提出了一种基于改进型ANF的三相EPLL控制策略，并用BF-PSO算法对控制器参数进行优化设计。
应用推荐