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Accurate Prediction of Phase Transitions in Compressed Sensing via a Connection to Minimax Denoising
Approximate Message Passing Lasso Group Lasso, Joint Sparsity, James- Stein, Minimax Risk over Nearly-Black Objects Minimax Risk of Soft Thresholding Minimax Risk of Firm Thresholding Minimax Shrinkage Nonconvex penalization State Evolution Total Variation Minimization Monotone Regression.
2015/8/21
Compressed sensing posits that, within limits, one can undersample a sparse signal and yet reconstruct it accurately. Knowing the precise limits to such undersampling is important both for theory and ...
针对含有未知外部干扰和不确定参数的非线性晶闸管控制串联补偿器(TCSC) 系统, 提出一种L2增益干扰抑制算法. 将minimax 方法引入耗散Hamilton 系统, 消除了不等式假设条件的约束; 构造检验函数, 推算出系统所能承受的最大干扰程度, 降低了传统干扰处理方法的保守性; 采用参数映射方法设计自适应律, 提高了参数跟踪效率. 最后通过机械功率和对地短路故障的仿真结果表明了所提出的控制方...