This work investigates the problem of adaptive measurement design for online subspace estimation from compressive linear measurements. We study the previously proposed Grassmannian rank-one online subspace estimation (GROUSE) algorithm with adaptively designed compressive measurements. We propose an adaptive measurement scheme that biases the measurement vectors towards the current subspace est...

When the variable of model is large, the Lasso method and the Adaptive Lasso method can effectively select variables. This paper prediction the rural residents’ consumption expenditure in China, based on respectively using the Lasso method and the Adaptive Lasso method. The results showed that both can effectively and accurately choose the appropriate variable, but the Adaptive Lasso method is ...

Broadband signal transmission over frequencyselective fading channel often requires accurate channel state information at receiver. One of the most attracting adaptive channel estimation methods is least mean square (LMS) algorithm. However, LMS-based method is often degraded by random scaling of input training signal. To improve the estimation performance, in this paper we apply the standard l...

Least mean square (LMS) based adaptive algorithms have been attracted much attention since their low computational complexity and robust recovery capability. To exploit the channel sparsity, LMS-based adaptive sparse channel estimation methods, e.g., L1-norm LMS or zero-attracting LMS (sparse LMS or ZA-LMS), reweighted zero attracting LMS (RZA-LMS) and Lp-norm LMS (LP-LMS), have been proposed b...

This work addresses an autonomous underwater vehicle (AUV) for applying nonlinear control which is capable of disturbance rejection via intelligent estimation of uncertainties. Adaptive radial basis function neural network (RBF NN) controller is proposed to approximate unknown nonlinear dynamics. The problem of designing an adaptive RBF NN controller was augmented with sliding mode robust term ...

Palmer, Andrew P., M.S., Purdue University, August, 2000. Indirect Adaptive Robust Control of Nonlinear Systems with Application to Electro-Mechanical/Hydraulic Systems. Major Professor: Dr. Bin Yao, School of Mechanical Engineering. The focus of the thesis is on the nonlinear model based control of systems with unknown parameters and uncertain nonlinearities. The objective is to maximize the a...

In this paper, the modularized adaptive backstepping designs are incorporated into the recently proposed adaptive robust control framework to synthesize indirect adaptive robust controllers that achieve not only good output tracking performance but also better parameter estimation processes to obtain accurate parameter estimates for secondary purposes such as machine health monitoring and progn...

This paper presents an empirical mode decomposition (EMD) based adaptive filter (AF) for channel estimation in OFDM system.  In this method, length of channel impulse response (CIR) is first approximated using Akaike information criterion (AIC). Then, CIR is estimated using adaptive filter with EMD decomposed IMF of the received OFDM symbol. The correlation and kurtosis measures are used to sel...