An adaptive beamforming algorithm named robust joint iterative optimizationdirection adaptive （RJIO-DA） is proposed for large-array scenarios. Based on the framework of minimum variance distortionless response （MVD...An adaptive beamforming algorithm named robust joint iterative optimizationdirection adaptive （RJIO-DA） is proposed for large-array scenarios. Based on the framework of minimum variance distortionless response （MVDR）, the proposed algorithm jointly updates a transforming matrix and a reduced-rank filter. Each column of the transforming matrix is treated as an independent direction vector and updates the weight values of each dimension within a subspace. In addition, the direction vector rotation improves the performance of the algorithm by reducing the uncertainties due to the direction error. Simulation results show that the RJIO-DA algorithm has lower complexity and faster convergence than other conventional reduced-rank algorithms.展开更多
在简单遗传算法的基础上,针对无功优化的动态、多目标、多约束以及非线性特点,提出了基于简单遗传算法的改进算法.改进遗传算法中采用了个体适应度函数的线性变换、归一化的选择方法,以及定向变异策略的应用.以ward & Hale 6节点标...在简单遗传算法的基础上,针对无功优化的动态、多目标、多约束以及非线性特点,提出了基于简单遗传算法的改进算法.改进遗传算法中采用了个体适应度函数的线性变换、归一化的选择方法,以及定向变异策略的应用.以ward & Hale 6节点标准测试系统为例对该算法进行了有效性验证.仿真结果表明,该方法对电力系统的无功优化效果良好.展开更多
基金Supported by National Natural Science Foundation of China （61304079, 61125306, 61034002）, the Open Research Project from SKLMCCS （20120106）, the Fundamental Research Funds for the Central Universities （FRF-TP-13-018A）, and the China Postdoctoral Science. Foundation （201_3M_ 5305_27）_ _ _
文摘为有致动器浸透和未知动力学的分离时间的系统的一个班的一个新奇最佳的追踪控制方法在这份报纸被建议。计划基于反复的适应动态编程(自动数据处理) 算法。以便实现控制计划，一个 data-based 标识符首先为未知系统动力学被构造。由介绍 M 网络，稳定的控制的明确的公式被完成。以便消除致动器浸透的效果， nonquadratic 表演功能被介绍，然后一个反复的自动数据处理算法被建立与集中分析完成最佳的追踪控制解决方案。为实现最佳的控制方法，神经网络被用来建立 data-based 标识符，计算性能索引功能，近似最佳的控制政策并且分别地解决稳定的控制。模拟例子被提供验证介绍最佳的追踪的控制计划的有效性。
基金supported by the National Science&Technology Pillar Program(2013BAF07B03)Zhejiang Provincial Natural Science Foundation of China(LY13F010009)
文摘An adaptive beamforming algorithm named robust joint iterative optimizationdirection adaptive （RJIO-DA） is proposed for large-array scenarios. Based on the framework of minimum variance distortionless response （MVDR）, the proposed algorithm jointly updates a transforming matrix and a reduced-rank filter. Each column of the transforming matrix is treated as an independent direction vector and updates the weight values of each dimension within a subspace. In addition, the direction vector rotation improves the performance of the algorithm by reducing the uncertainties due to the direction error. Simulation results show that the RJIO-DA algorithm has lower complexity and faster convergence than other conventional reduced-rank algorithms.
文摘在简单遗传算法的基础上,针对无功优化的动态、多目标、多约束以及非线性特点,提出了基于简单遗传算法的改进算法.改进遗传算法中采用了个体适应度函数的线性变换、归一化的选择方法,以及定向变异策略的应用.以ward & Hale 6节点标准测试系统为例对该算法进行了有效性验证.仿真结果表明,该方法对电力系统的无功优化效果良好.
