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- An improved PSO algorithm was presented and applied to optimal the PID parameters of electromotor. 为此,提出一种改进的PSO优化算法,并将该算法应用于电机控制系统的PID参数优化设计。
- To get better optimization results, an improved PSO algorithm named IPSO including variance mechanism and local updating mechanism was presented. 为提高其优化求解效果，引入变异机制及局部更新机制对粒子群优化算法进行改进。
- The test results based on three benchmark functions show that the improved PSO algorithm has a good performance on global convergency and convergence precision. 基于3个基准测试函数的测试结果显示改进粒子群优化算法具有较好的全局收敛性和收敛精度。
- An improved PSO (particle swarm optimization) algorithm is presented which well addresses slow convergence speed and low calculation precision in the basic PSO algorithm. 摘要提出了一种改进的粒子群算法，很好地解决了基本粒子群算法中易陷入局部最优的缺点。
- Subsequently,we briefly discuss the identifiability of the parameters.Finally,in order to find the optimal parameters of the identification model,an improved PSO algorithm is constructed. 最后结合模型特点构造了一种改进的粒子群优化(PSO)算法求得最优参数,并利用所得的参数进行过程仿真。
- In order to overcome the shortcomings that standard Particle Swarm Optimization（PSO） traps into local optima easily and has a low convergence accuracy，an improved PSO algorithm is proposed. 摘要 针对标准粒子群算法容易陷入局部最优、收敛精度低的缺点，提出了一种改进的粒子群算法。
- A Study of Application of An Improved PSO Algorithm in BP Network 一种改进的粒子群算法在BP网络中的应用研究
- Neural Network Ensembles Based on Improved PSO Algorithm 基于改进的PSO算法的神经网络集成
- Improved PSO algorithm and its application in short-term generation scheduling 基于改进PSO算法的短期发电计划研究
- improved PSO algorithm 改进PSO算法
- The PSO algorithm is improved to make it more intelligent, which is used in mute planning of the cruise missile. 对近年来出现的粒子群算法进行了改进以使其更具智能化，将它应用于巡航导弹路径规划问题，并进行了仿真计算。
- An improved fuzzy adaptive PID algorithm (IFPID) is thus proposed, using PSO algorithm to optimize the preprocessed membership function. 针对这种情况，提出改进的模糊自适应PID控制算法(IFPID)，利用微粒群算法对经过预处理的隶属度函数进行优化。
- Considering the premature convergence limitation of the basic particle swarm optimizer (PSO) algorithm, an improved adaptive PSO algorithm with mutation was proposed. 针对粒子群算法易早熟收敛的局限性，提出了一种带变异的改进自适应粒子群优化(PSO)算法。
- Then, the improved PSO is applied to optimization of the structure and parameters in NN (neural network). 改进的粒子群算法被用于优化神经网络的结构和参数，结果表明：不但网络的结构得到控制，而且泛化性能有了较大的提高。
- Experimental results indicate that the ANN based on improved PSO can reduce the times of training and MSE effectively. 仿真实验表明：基于改进型粒子群优化算法的神经网络可以有效降低训练次数和均方误差。
- It is showed that the PSO algorithm is an efficient method to deal with NLP problems. 结果表明 ,PSO算法在使用的普遍性、求解的准确性方面都优于一般的算法 ,是一种有效的求解NLP问题的方法
- To solve the premature convergence problem of the Particle Swarm Optimization (PSO), an improved PSO method was proposed. 针对粒子群优化算法早熟收敛现象，提出了一种改进的粒子群优化算法。
- At last, applications of PSO algorithm are discussed, and further research issues and some suggestions are given. 最后归纳了PSO算法的应用概况，并就PSO算法进一步的研究工作进行了探讨和展望。
- The experimental results show that the PSO algorithm provides an effective method to estimate parameters of NSM. 实验结果表明：微粒群算法为非线性系统模型参数估计提供了一种有效的途径。
- The results of simulation show that the proposed PSO algorithm has its high validity, robustness and efficiency. 仿真结果表明，改进的PSO算法有更好的搜索效率，取得了较好的效果。
