利用粒子群的优化算法，建立侵彻子母弹最佳抛撒高度的求解模型，并进行了仿真计算。
By using Particle Swarm Optimization (PSO), a model was built up for calculating the optimum dispersion height of intrusive submunition dispenser, and mathematical simulation was carried out.
论文研究了动态无功优化的数学模型和粒子群优化算法。
The paper studies the mathematical model of the dynamic reactive optimization and the particle swarm optimization (PSO).
该模型用粒子群算法求解，作者给出了解法步骤。
The model was solved by adopting the particle swarm algorithm, and the solution steps were presented in the paper.
针对此模型，采用改进粒子群优化算法进行求解。
The proposed model is solved by improved particle swarm optimization (PSO) algorithm.
基于粒子群算法运用随机模拟和模糊模拟相结合的技术，给出了一种求解该规划模型的混合智能算法。
Desgined a mixed intelligent arithmetic by using the technique of combing the stochastic simulation with fuzzy simulation and particle swarm optimization algorithm to solve this kind of problems.
针对贷款组合优化决策模型的求解问题，论文提出了用于求解该问题的二进制粒子群算法，并阐明了算法的具体实现过程。
This paper brings forward the binary improved particle swarm optimization algorithm for decision of loans combinatorial optimization problem, and illustrates the detailed realization of the algorithm.
基于此模型，本文还进一步分析了引入pmu后对状态估计精度的影响，从而提出了基于粒子群优化算法(PSO)的PMU配置。
Furthermore, the precision improvement of state estimation due to incorporation of PMU is analyzed based on the model and particle swarm optimization (PSO) is applied to solve the placement of PMU.
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.
为此，根据企业利润最大化原则建立机组经济运行数学模型，并用改进粒子群算法对模型优化求解。
In this paper, based on the principle of maximum profit, a mathematical model of unit which is unit economy operation is presented.
用粒子群算法对融合模型进行优化得到PSO优化模型。
Particle Swarm Optimization(PSO)is used to select parameters for the model.
基于结构可靠度指标的物理含义，建立了水库泄洪风险计算优化模型，并引进了粒子群全局优化算法对该模型进行求解。
Based on the physical meanings of structural reliability index, the optimization model of flood discharging risk is established in this paper, and the global PSO is introduced to solve the model.
基于混沌粒子群优化算法对优化数学模型进行了求解。
The global optimal solution is obtained based on chaotic particle swarm optimization algorithm.
组卷算法研究方面，首先为组卷行为建立一个数学模型，并提出应用粒子群优化(PSO)算法组卷。
In the researches of generating paper algorithm, a mathematic model is firstly founded, and then particle swarm optimization (PSO) algorithm is applied to paper's generating.
将粒子群算法引入大坝安全监控领域，并结合多元回归统计模型，建立基于粒子群算法的混凝土坝变形预报模型。
A concrete dam deformation forecasting model is established based on the particle swarm optimization (PSO) algorithm and the traditional multi-statistical regression model.
采用变异粒子群优化算法确定了模型参数，并取得了较好的效果。
The mutation particle swarm optimization algorithm is employed to determine the constitutive parameters and it is proved to present good performance.
同时利用粒子群算法优化小波最小二乘支持向量机的参数，避免了人为选择参数的盲目性，从而提高了模型的预测精度。
The adaptive particle swarm optimization is used to optimize the parameters of SVM so as to avoid artificial arbitrariness and enhance the forecast accuracy.
通过应用实例证明，将改进的粒子群优化算法应用到电力负荷组合预测模型的权重求解是可行的。
Application examples show that it is feasible to apply the improved PSO to the weight solution of power load combination forecasting model.
该模型一方面采用粒子群算法优化投影指标函数及逻辑斯谛曲线函数参数，确保了模型参数的准确性；
The model, on the one hand, uses the PSO to optimize the projection index function and the parameters of LCF so as to ensure the accuracy of the parameters used in the model.
采用粒子群算法对镗孔加工尺寸误差人工神经网络预测模型进行优化。
This paper presented particle swarm optimization (PSO) technique to train multi layer artificial neural network for predicting model of diameter errors of boring processes.
建立了集合划分问题的优化数学模型，结合遗传算法的思想提出的粒子群算法来解决集合划分问题。
This paper shows us a study of the division and combination problem of DE on the basis of division and combination history along with a division and combination perspective.
提出了基于场景的多目标随机规划模型来构建不确定市场需求环境下的能力计划问题模型，并用改进的多目标粒子群优化算法求解。
We formulated scenario based multi-objective stochastic programming model to describe the problem of capacity planning under uncertainty and applied improved Multi-Objective PSO (MOPSO) to solve it.
提出了基于场景的多目标随机规划模型来构建不确定市场需求环境下的能力计划问题模型，并用改进的多目标粒子群优化算法求解。
We formulated scenario based multi-objective stochastic programming model to describe the problem of capacity planning under uncertainty and applied improved Multi-Objective PSO (MOPSO) to solve it.
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