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FastICA算法，又称固定点(Fixed-Point)算法，该算法采用了定点迭代的优化算法，使得收敛更加快速、稳健。-FastICA algorithm, also known as fixed-point (Fixed-Point) algorithm, which uses fixed-point iteration of the optimization algorithm, making convergence more rapid, and robust.
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介绍了一种基于最，j,-最大原理整定鲁棒PID控制器参数的方法，并应用MA ⅡAB
优化工具箱函数按非线性二次规划法搜索合理的PID参数。仿真结果表明，优化函数寻找
最佳PID参数简便可靠；当模型在一定范围内变化时，按该方法整定的鲁棒PID控制器均
能保证控制系统的动态性能良好。-Introduced based on the most, j,- setting the maximum principle method of robust PID controller paramet
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PSO optimization. PSO is a robust stochastic optimization technique based on the movement and intelligence of swarms.
PSO applies the concept of social interaction to problem solving.
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Reliability and robust design optimization, 可靠性分析，与鲁棒性分析，设计优化-Reliability and robust design optimization
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ue to the volume conduction multichannel electroencephalogram (EEG) recordings
give a rather blurred image of brain activity. Therefore spatial filters are
extremely useful in single-trial analysis in order to improve the signal-to-noise
ratio.
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This paper presents a method for optimum tuning of Fractional PID controllers using PSO algorithm which have fractional or integer order transfer functions. Particle swarm optimization (PSO) is a robust stochastic optimization technique based on the
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现有的向量加权稳健波束形成方法只有在指向误差较小的情况下才能有效估计目标的信号功率；矩阵加权波束形成方法在指向误差较大时，虽然可以估计目标的信号功率，但是它的系统实现复杂度与向量加权稳健波束形
成方法相比较大。针对以上问题，该文提出基于半正定秩松弛(SDR)方法的稳健波束形成，该方法优化模型中的目标函数与Capon 算法的目标函数相同，优化变量为加权向量的协方差矩阵，并约束方向图的主瓣幅度波动范围、旁瓣电平，协方差矩阵的秩为1。-The existing vector weighted ro
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多传感器分配是传感器网应用过程中的一个关键问题，文章对多种方法进行了分析比较，指出了各种方法的优缺点及适用场合，值得借鉴-Sensor deployment is a fundamental issue in sensor
networks. Studies on the deployment problem are concentrated
on deploying sensors to cover assigned areas or set ofpoints
efficientl
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全书共分十五章，第1章至第7章为入门篇，介绍Isight的界面、集成、试验设计、数值和全局优化算法；第8章至第13章为提高篇，全面介绍近似建模、组合优化策略、多目标优化、蒙特卡洛模拟、田口稳健设计和6Sigma品质设计方法DFSS（Design For 6Sigma）的相关知识。-The book is divided into fifth chapter, Chapter 1 to Chapter 7 for the introductory chapter, introduced Isigh
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This paper suggests that a simple modification to the initial search direction can also substantially improve the training efficiency of almost all major optimization methods. It was discovered that if the initial search direction is locally modified
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This paper concerns a method for digital circuit optimization based on formulating the problem as a geometric program
(GP) or generalized geometric program (GGP), which can be transformed to a convex optimization problem and then very
efficiently
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The Art of CAE-Based Parametric Variation Analysis from Sensitivity Analysis to Robust Design Optimization
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根据常规鲁棒方法 太过于保守 随机优化方法不够准确 可用于电力系统优化调度(Conventional robust methods are too conservative stochastic optimization methods are not accurate enough to be used for optimal dispatching of power systems)
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