Learning algorithms for adaptive digital filtering

In this thesis, we consider the problem of parameter optimisation in adaptive digital filtering. Adaptive digital filtering can be accomplished using both Finite Impulse Response (FIR) filters and Infinite Impulse Response Filters (IIR) filters. Adaptive FIR filtering algorithms are well established. However, the potential computational advantages of IIR filters has led to an increase in research on adaptive IIR filtering algorithms. These algorithms are studied in detail in this thesis and the limitations of current adaptive IIR filtering algorithms are identified. New approaches to adaptive IIR filtering using intelligent learning algorithms are proposed. These include Stochastic Learning Automata, Evolutionary Algorithms and Annealing Algorithms. Each of these techniques are used for the filtering problem and simulation results are presented showing the performance of the algorithms for adaptive IIR filtering. The relative merits and demerits of the different schemes are discussed. Two practical applications of adaptive IIR filtering are simulated and results of using the new adaptive strategies are presented. Other than the new approaches used, two new hybrid schemes are proposed based on concepts from genetic algorithms and annealing. It is shown with the help of simulation studies, that these hybrid schemes provide a superior performance to the exclusive use of any one scheme

Structures and Algorithms for Two-Dimensional Adaptive Signal Processing

Coordinated Science Laboratory was formerly known as Control Systems LaboratoryOpe

Development and applications of adaptive IIR and subband filters

Adaptive infinite impulse response (IIR) filter is a challenging research area. Identifiers and Equalizers are among the most essential digital signal processing devices for digital communication systems. In this study, we consider IIR channel both for system identification and channel equalization purposes. We focus on four different approaches: Least Mean Square (LMS), Recursive Least Square (RLS), Genetic Algorithm (GA) and Subband Adaptive Filter (SAF). ). The performance of conventional LMS and RLS based IIR system identification and channel equalization are found with the help of computer simulations. And also the convergence speed and the ability to locate the global optimum solution using a population based algorithm named Genetic Algorithm is given

Commande robuste et calibrage des systèmes de contrôle actif de vibrations

Dans cette thèse, nous présentons des solutions pour la conception des systèmes de contrôle actif de vibrations. Dans la première partie, des méthodes de contrôle par action anticipatrice (feedforward) sont développées. Celles-ci sont dédiées à la suppression des perturbations bande large en utilisant une image de la perturbation mesurée par un deuxième capteur, en amont de la variable de performance à minimiser. Les algorithmes présentés dans cette mémoire sont conçus pour réaliser de bonnes performances et maintenir la stabilité du système en présence du couplage positif interne qui apparaît entre le signal de commande et l'image de la perturbation. Les principales contributions de cette partie sont l'assouplissement de la condition de Stricte Positivité Réelle (SPR) par l'utilisation des algorithmes d'adaptation Intégrale + Proportionnelle et le développement de compensateurs à action anticipatrice (feedforward) sur la base de la paramétrisation Youla-Kučera. La deuxième partie de la thèse concerne le rejet des perturbations bande étroite par contre-réaction adaptative (feedback). Une méthode d'adaptation indirecte est proposée pour le rejet de plusieurs perturbations bande étroite en utilisant des filtres Stop-bande et la paramétrisation Youla-Kučera. Cette méthode utilise des Filtres Adaptatifs à Encoche en cascade pour estimer les fréquences de perturbations sinusoïdales puis des Filtres Stop-bande pour introduire des atténuations aux fréquences estimées. Les algorithmes sont vérifiés et validés sur un dispositif expérimental disponible au sein du département Automatique du laboratoire GIPSA-Lab de Grenoble.In this thesis, solutions for the design of robust Active Vibration Control (AVC) systems are presented. The thesis report is composed of two parts. In the first one, feedforward adaptive methods are developed. They are dedicated to the suppression of large band disturbances and use a measurement, correlated with the disturbance, obtained upstream from the performance variable by the use of a second transducer. The algorithms presented in this thesis are designed to achieve good performances and to maintain system stability in the presence of the internal feedback coupling which appears between the control signal and the image of the disturbance. The main contributions in this part are the relaxation of the Strictly Positive Real (SPR) condition appearing in the stability analysis of the algorithms by use of Integral + Proportional adaptation algorithms and the development of feedforward compensators for noise or vibration reduction based on the Youla-Kučera parameterization. The second part of this thesis is concerned with the negative feedback rejection of narrow band disturbances. An indirect adaptation method for the rejection of multiple narrow band disturbances using Band-Stop Filters (BSF) and the Youla-Kučera parameterization is presented. This method uses cascaded Adaptive Notch Filters (ANF) to estimate the frequencies of the disturbances' sinusoids and then, Band-stop Filters are used to shape the output sensitivity function independently, reducing the effect of each narrow band signal in the disturbance. The algorithms are verified and validated on an experimental setup available at the Control Systems Department of GIPSA-Lab, Grenoble, France.SAVOIE-SCD - Bib.électronique (730659901) / SudocGRENOBLE1/INP-Bib.électronique (384210012) / SudocGRENOBLE2/3-Bib.électronique (384219901) / SudocSudocFranceF

Existence of Stationary Points for Reduced-Order Hyperstable Adaptive IIR Filters

We establish existence of asymptotic stationary points for a class of adaptive IIR filtering algorithms, including (S)HARF, the Feintuch algorithm, and Landau's algorithm, for reduced-order cases. We show first that the nonlinear equations characterizing a stationary point admit a solution giving rise to a stable transfer function, when the input is white noise. We then show that an analytic procedure to construct the solution may be reduced to the NevanlinnaPick interpolation problem. The white noise assumption on the input simplifies the mathematics of an already difficult problem, although the existence proof appears extendable to correlated inputs as well. 1. INTRODUCTION Most convergence results for adaptive IIR filters assume a sufficient order setting: the degree of the identifier is at least as large as that of the unknown system. Comparatively few convergence results are available for more realistic reducedorder settings; the equations characterizing stationary points are oft..

Signal modelling: A versatile approach for the automatic analysis of the electroencephalogram

Despite recent advances in brain monitoring techniques, the electroencephalogram (EEG) is still widely used in the diagnosis and monitoring of epilepsy. To increase its effectiveness, long-term monitoring of patients was proposed but the large volume of recorded EEG signals produced, made their traditional interpretation by human experts difficult and automatic EEG analysis was proposed as an alternative. This Thesis is concerned, primarily, with the on-line detection of epileptic transients (spikes) in the interictal EEG signals of patients. A review of previous methods, revealed that the limited success of automatic analysis systems was linked to the vagueness of neurophysiological definitions and the subjectiveness of human interpretation, which is based on experience. To address these issues, it was realized that a common point of reference is required for the integration of medical and signal processing expertise, which could be provided by a model of the signal. Early attempts to develop such a model are described. These led to the development of spike detectors based on the derivatives of the EEG. Later, by describing medical definitions with signal processing terminology, a comprehensive model of the signal was constructed. This was based on its decomposition into background activity, spikes, transients and noise and describing each one of them in terms of simple, random signals and quasi-linear systems. This suggested a method of analysis based on inverse modelling for the decomposition of the EEG. The model for transients was estimated off-line. An on-line system, consisting of adaptive prediction error systems, constrained all-pole adaptive systems and a basic signal detection procedure was implemented. Several alternative adaptive realizations were investigated. The spike detection procedure was generalized for the detection of other transients. Finally this procedure was replaced by a Multi-Layer Perceptron neural network, whose inherent ability to learn by example is important, as it provides the means to incorporate medical experience without requiring its explicit quantification. The system is flexible and its extension to detect any number of transients is demonstrated. The method may be applied to other signals and improved by new developments in signal processing

Proceedings of the 9th MIT/ONR workshop on C3 Systems, held at Naval Postgraduate School and Hilton Inn Resort Hotel, Monterey, California June 2 through June 5, 1986

GRSN 627729"December 1986."Includes bibliographical references and index.Sponsored by Massachusetts Institute of Technology, Laboratory for Information and Decision Systems, Cambridge, Mass., with support from the Office of Naval Research. ONR/N00014-77-C-0532(NR041-519) Sponsored in cooperation with IEEE Control Systems Society, Technical Committee on C.edited by Michael Athans, Alexander H. Levis