Considering that the particle swarm optimization (PSO) algorithm is quite simple and easy to implement, it was used to estimate the nonlinear model parameters in this paper.
粒子群算法操作简便、容易实现且全局搜索功能较强，适用于非线性参数估计。
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 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。
It makes the search space some sub-region, USES the PSO algorithm to optimize in each region, compares these sub - region global optimums and finds out the search space global optimums.
将搜索空间划分成若干个子区域，在各个子区域中均使用标准P SO算法进行寻优，通过比较各个子区域的全局最优解，从而得出整个搜索空间的全局最优。
Comparing with the traditional PSO algorithm, it possesses more biological characteristics and is much more closed to the real rules of birds swarm's foraging.
与传统粒子群算法相比，它更具有生物特性，更接近于鸟群觅食的真实规律。
The new algorithms are tested and compared with the standard PSO. It is proved that a new method is a simple and effective algorithm.
经过与基本粒子群算法比较测试，证实它是一种简单有效的算法。
It presents a method of optimum placement of sensors in gearbox based PSO algorithm to solve the problem of sensors layout and localization.
提出了基于粒子群优化的齿轮箱传感器优化配置方法，解决多测点传感器的布置和定位问题。
A new adaptive filtering model based on particle swarm optimization (PSO) algorithm is proposed and designed. It is proved to be efficient and effective in the computer simulation example test.
提出并设计了一种基于粒子群优化算法的振动信号的自适应滤波模型。该滤波模型在计算机仿真测试中，获得了很高的效率和良好的结果。
A new adaptive filtering model based on particle swarm optimization (PSO) algorithm is proposed and designed. It is proved to be efficient and effective in the computer simulation example test.
提出并设计了一种基于粒子群优化算法的振动信号的自适应滤波模型。该滤波模型在计算机仿真测试中，获得了很高的效率和良好的结果。
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