/pdf/convex-analysisnoer-san-nojin-zhan-nituite-5dl2rbmoyr.pdf

Convex Analysisの二,三の進展について

Information Theory and Reliable Communication

Introduction to linear and nonlinear programming

Conditions under which a nonlinear system can be rendered passive via smooth state feedback are derived. It is shown that, as in the case of linear systems, this is possible if and only if the system in question has relative degree one and is weakly minimum phase. It is proven that weakly minimum phase nonlinear systems with relative degree one can be globally asymptotically stabilized by smooth state feedback, provided that suitable controllability-like rank conditions are satisfied. This result incorporates and extends a number of stabilization schemes recently proposed for global asymptotic stabilization of certain classes of nonlinear systems. >

Passivity, feedback equivalence, and the global stabilization of minimum phase nonlinear systems

http://math.fau.edu/wang/pdf-file/journal/dt-iss-automatica.pdf

Input-to-state stability for discrete-time nonlinear systems

A new computationally efficient algorithm for sequential least-squares (LS) estimation is presented in this paper. This fast a posteriori error sequential technique (FAEST) requires 5p MADPR (multiplications and divisions per recursion) for AR modeling and 7p MADPR for LS FIR filtering, where p is the number of estimated parameters. In contrast the well-known fast Kalman algorithm requires 8p MADPR for AR modeling and 10p MADPR for FIR filtering. The increased computational speed of the introduced algorithm stems from an alternative definition of the so-called Kalman gain vector, which takes better advantage of the relationships between forward and backward linear prediction.

A fast sequential algorithm for least-squares filtering and prediction

We develop a recursive L1-regularized least squares (SPARLS) algorithm for the estimation of a sparse tap-weight vector in the adaptive filtering setting. The SPARLS algorithm exploits noisy observations of the tap-weight vector output stream and produces its estimate using an expectation-maximization type algorithm. We prove the convergence of the SPARLS algorithm to a near-optimal estimate in a stationary environment and present analytical results for the steady state error. Simulation studies in the context of channel estimation, employing multipath wireless channels, show that the SPARLS algorithm has significant improvement over the conventional widely used recursive least squares (RLS) algorithm in terms of mean squared error (MSE). Moreover, these simulation studies suggest that the SPARLS algorithm (with slight modifications) can operate with lower computational requirements than the RLS algorithm, when applied to tap-weight vectors with fixed support.

/pdf/sparls-the-sparse-rls-algorithm-28szhwec14.pdf

SPARLS: The Sparse RLS Algorithm

Basic concepts and algorithmic schemes, N. Kalouptsidis and S. Theodoridis general structure of adaptive algorithms - adaptation and tracking, L. Ljung the LMS family, W. Sethares fast transversal RLS algorithms, D. Slock and T. Kailath lattic algorithms, F. Ling the QR family, J.G. McWhirter and I. Proudler circular lattices, Sakai channel equalization, C.F.N. Cowan echo cancellation, F. Ling interference rejections and channel estimation for spread-spectrum comms, R. Iltis spectral analysis for adaptive signal processing, S. Theodoridis and N. Kalouptsidis neural networks, S. Haykin.

/pdf/adaptive-system-identification-and-signal-processing-55yks4up6g.pdf

Adaptive system identification and signal processing algorithms

Signals systems signal transforms convolutional type forms of systems finite recursive representations algorithms stability of dynamical systems applications.

Nicholas Kalouptsidis

Papers

A fast sequential algorithm for least-squares filtering and prediction

SPARLS: The Sparse RLS Algorithm

Adaptive system identification and signal processing algorithms

Signal Processing Systems: Theory and Design

Adaptive algorithms for sparse system identification