1) immune particle swarm optimization
免疫粒子群优化
1.
Application of immune particle swarm optimization to job-shop scheduling problem;
免疫粒子群优化算法在车间作业调度中的应用
2.
Application of immune particle swarm optimization algorithm in image fusion
免疫粒子群优化算法在图像融合中的应用
3) Immune Particle Swarm Optimization(IPSO)
免疫微粒群优化算法
1.
Comparison of simulated results between Immune Particle Swarm Optimization(IPSO),PSO and DWIPSO shows that IPSO.
使用常用的基准函数对算法进行了仿真验证工作,通过与全局微粒群优化算法、变惯性权值微粒群优化算法的对比表明,免疫微粒群优化算法(IPSO)在搜索速度和全局寻优方面具有一定的优势。
4) immunity-particle swarm
免疫粒子群
1.
The optimized algorithm is immunity-particle swarm which could avoide getting into local best place.
根据机械部件磨损机理复杂、磨损量预测难精确的特点,提出基于免疫粒子群参数优化的最小二乘支持向量机方法预测磨损量。
2.
A method of parameter optimized least square support vector machine was given out to predict more data based on the small sample data obtained from experiments,and the parameter\' optimization made use of the immunity-particle swarm algorithm,so the sample\'s number of bearing\'s wear life was expanded.
应用免疫粒子群算法对最小二乘支持向量机的建模参数进行了优化。
5) immune particle swarm algorithm
免疫粒子群算法
1.
Due to the stubborn nature of the JSP,a method based on immune particle swarm algorithm(IPA) is initially brought forward to solve JSP.
针对车间作业调度这个优化问题的难处理性,提出了基于免疫粒子群算法(IPA)的JSP求解方法。
6) particle swarm optimization(PSO)
粒子群优化
1.
With the introduction of punishment function and the appropriate modification of objective function,a sintering blending optimization algorithm is proposed,which takes full advantage of the global search ability of particle swarm optimization(PSO) algorithm and the local search ability of conjugate gradient algorithm with constraints.
在引入惩罚函数和对目标函数进行适当修改的前提下,充分利用粒子群优化算法的全局搜索能力和约束条件下共轭梯度法的局部搜索能力,设计了烧结配料优化算法。
2.
In order to solve the problems of low-precision and slow control of the traditional algorithms in the pattern recognition and control of flatness,the neural network trained by hybrid algorithms of particle swarm optimization(PSO) and back propagation(BP)is introduced.
为了解决传统的板形识别与控制中的识别精度低,控制速度慢等问题,将粒子群优化(particle swarm optimization,PSO)算法和误差反传递(back propagation,BP)算法混合训练的PSO-BP网络引入到板形的识别与控制中。
3.
HPSO introduces evolutionary algorithm into particle swarm optimization(PSO),thus can get higher precision and faster convergence spe.
由于电力负荷内在的非线性特性,传统基于梯度搜索的参数辨识技术可能陷入局部最优,影响了预测精度,故提出了混合进化和粒子群优化算法。
补充资料：颜料β粒子群
分子式：
CAS号：
性质：系指由多个原级粒子或二次粒子或它们两者通过比较弱的物理力，以粒子与粒子的棱或角相接触而形成的疏松的集合体，又称颜料三次粒子(pigment tertiary particle)，也称颜料β粒子群(β-particle cluster of pigment)。由于粒子间的结合力弱，可通过颜料分散过程使其分离开，故它本身不能在颜料最终应用系统中作为一个实体而存在。是由颜料在制造和贮存过程中产生的。附聚体(agglomerate)与聚集体(aggregate)，絮凝体(flocculate)不可混淆。
CAS号：
性质：系指由多个原级粒子或二次粒子或它们两者通过比较弱的物理力，以粒子与粒子的棱或角相接触而形成的疏松的集合体，又称颜料三次粒子(pigment tertiary particle)，也称颜料β粒子群(β-particle cluster of pigment)。由于粒子间的结合力弱，可通过颜料分散过程使其分离开，故它本身不能在颜料最终应用系统中作为一个实体而存在。是由颜料在制造和贮存过程中产生的。附聚体(agglomerate)与聚集体(aggregate)，絮凝体(flocculate)不可混淆。
说明：补充资料仅用于学习参考，请勿用于其它任何用途。
参考词条