Robust and Regularized Algorithms for Vehicle Tractive Force Prediction and Mass Estimation

This work provides novel robust and regularized algorithms for parameter estimation with applications in vehicle tractive force prediction and mass estimation. Given a large record of real world data from test runs on public roads, recursive algorithms adjusted the unknown vehicle parameters under a broad variation of statistical assumptions for two linear gray-box models

Regularized System Identification

This open access book provides a comprehensive treatment of recent developments in kernel-based identification that are of interest to anyone engaged in learning dynamic systems from data. The reader is led step by step into understanding of a novel paradigm that leverages the power of machine learning without losing sight of the system-theoretical principles of black-box identification. The authors’ reformulation of the identification problem in the light of regularization theory not only offers new insight on classical questions, but paves the way to new and powerful algorithms for a variety of linear and nonlinear problems. Regression methods such as regularization networks and support vector machines are the basis of techniques that extend the function-estimation problem to the estimation of dynamic models. Many examples, also from real-world applications, illustrate the comparative advantages of the new nonparametric approach with respect to classic parametric prediction error methods. The challenges it addresses lie at the intersection of several disciplines so Regularized System Identification will be of interest to a variety of researchers and practitioners in the areas of control systems, machine learning, statistics, and data science. This is an open access book

Regularized Estimation of High-dimensional Covariance Matrices.

Many signal processing methods are fundamentally related to the estimation of covariance matrices. In cases where there are a large number of covariates the dimension of covariance matrices is much larger than the number of available data samples. This is especially true in applications where data acquisition is constrained by limited resources such as time, energy, storage and bandwidth. This dissertation attempts to develop necessary components for covariance estimation in the high-dimensional setting. The dissertation makes contributions in two main areas of covariance estimation: (1) high dimensional shrinkage regularized covariance estimation and (2) recursive online complexity regularized estimation with applications of anomaly detection, graph tracking, and compressive sensing. New shrinkage covariance estimation methods are proposed that significantly outperform previous approaches in terms of mean squared error. Two multivariate data scenarios are considered: (1) independently Gaussian distributed data; and (2) heavy tailed elliptically contoured data. For the former scenario we improve on the Ledoit-Wolf (LW) shrinkage estimator using the principle of Rao-Blackwell conditioning and iterative approximation of the clairvoyant estimator. In the latter scenario, we apply a variance normalizing transformation and propose an iterative robust LW shrinkage estimator that is distribution-free within the elliptical family. The proposed robustified estimator is implemented via fixed point iterations with provable convergence and unique limit. A recursive online covariance estimator is proposed for tracking changes in an underlying time-varying graphical model. Covariance estimation is decomposed into multiple decoupled adaptive regression problems. A recursive recursive group lasso is derived using a homotopy approach that generalizes online lasso methods to group sparse system identification. By reducing the memory of the objective function this leads to a group lasso regularized LMS that provably dominates standard LMS. Finally, we introduce a state-of-the-art sampling system, the Modulated Wideband Converter (MWC) which is based on recently developed analog compressive sensing theory. By inferring the block-sparse structures of the high-dimensional covariance matrix from a set of random projections, the MWC is capable of achieving sub-Nyquist sampling for multiband signals with arbitrary carrier frequency over a wide bandwidth.Ph.D.Electrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/86396/1/yilun_1.pd

Segment phoneme classification from speech under noisy conditions: Using amplitude-frequency modulation based two-dimensional auto-regressive features with deep neural networks

This thesis investigates at the acoustic-phonetic level the noise robustness of features derived using the AM-FM analysis of speech signals. The analysis on the noise robustness of these features is done using various neural network models and is based on the segment classification of phonemes. This analysis is also extended and the robustness of the AM-FM based features is compared under similar noise conditions with the traditional features such as the Mel-frequency cepstral coefficients(MFCC). We begin with an important aspect of segment phoneme classification experiments which is the study of architectural and training strategies of the various neural network models used. The results of these experiments showed that there is a difference in the training pattern adopted by the various neural network models. Before over-fitting, models that undergo pre-training are seen to train for many epochs more than their opposite models that do not undergo pre-training. Taking this difference in training pattern into perspective and based on phoneme classification rate the Gaussian restricted Boltzmann machine and the single layer perceptron are selected as the best performing model of the two groups, respectively. Using the two best performing models for classification, segment phoneme classification experiments under different noise conditions are performed for both the AM-FM based and traditional features. The experiments showed that AM-FM based frequency domain linear prediction features with or without feature compensation are more robust in the classification of 61 phonemes under white noise and 0 $dB$ signal-to-noise ratio(SNR) conditions compared to the traditional features. However, when the phonemes are folded to 39 phonemes, the results are ambiguous under all noise conditions and there is no unanimous conclusion as to which feature is most robust