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This article presents particle swarm optimization (PSO)-based optimal gain tuning of proportional integral (PI)
speed controller in an induction motor (IM) drive (30 hp) with mine hoist load diagram
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During the last decade, many variants of the original
particle swarm optimization (PSO) algorithm have been
proposed. In many cases, the difference between two variants can
be seen as an algorithmic component being present in one variant
but
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子群优化算法中,粒子群由多个粒子组成,每个粒子的位置代表优化问题在D维搜索空间中潜在的解。根据各自的位置,每个粒子用一个速度来决定其飞行的方向和距离,然后通过优化函数计算出一个适应度函数值(fitness)。-Subgroup of particle swarm optimization algorithm is composed by a number of particles, each particle' s position represents to optimize the p
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多峰值函数优化中,基本粒子群算法进化后期收敛速度较慢,提出一种具有可控速度因子的改进粒子群算法.-Multimodal function optimization, PSO late evolutionary convergence is slow, with a controllable speed improvements proposed particle swarm optimization factors.
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针对感应电机扩展卡尔曼滤波器转速估计中难以取得卡尔曼滤波器系统噪声矩阵和测量噪声矩阵最优值的问题，提出了一种基于改进粒子群算法优化的扩展卡尔曼滤波器转速估计方法。算法通过融合遗传算法和粒子群算法的优点，采用可调整的算法模型对粒子群算法进行改进，将改进的粒子群算法对扩展卡尔曼滤波器中的系统噪声矩阵和测量噪声矩阵进行优化处理，将优化后的卡尔曼滤波器应用于感应电机转速估计。-
Extended K
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一个例子的最佳位置和沿着当前速度和惯性方向的邻元素被用来决定下一个例子的位子。多目标粒子群的优化算法简介如下-An example of the best location and along the current speed and direction of inertia neighbors are used to determine the next example of the seat. Introduction to multi-objective optimization alg
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