الصفحة 1
الصفحة 1
Modern Testing Techniques for Structural Systems : Dynamics and Control
The articles in this book describe new developments in the area of structural testing, particularly those based upon the principle of fusing numerical and experimental methods such as real-time dynamic substructuring and hardware-in-the loop testing. In addition to the hybrid methods, chapters on the latest develoments in more established techniques, such as shaking table testing, provide a completely up-to-date survey of structural testing methods.
Modelling and Identification with Rational Orthogonal Basis Functions
Models of dynamical systems are of great importance in almost all fields of science and engineering and specifically in control, signal processing and information science.
Modeling, Estimation and Control : Festschrift in Honor of Giorgio Picci on the Occasion of his Sixty-Fifth Birthday
Coefficients of Variations in Analysis of Macro-Policy Effects: An example of two-parameter Poisson-Dirichlet distributions.- How Many Experiments Are Needed to Adapt?- A Mutual Information Based Distance for Multivariate Gaussian Processes.- Differential Forms and Dynamical Systems.- An Algebraic Framework for Bayes Nets of Time Series.- A Birds Eye View on System Identification.- Further Results on the Byrnes-Georgiou-Lindquist Generalized Moment Problem.- Factor Analysis and Alternating Minimization.- Tensored PolynomialModels.- Distances Between Time-Series and Their Autocorrelation Statistics.- Global Identifiability of Complex Models, Constructed from Simple Submodels.- Identification of Hidden MarkovModels - Uniform LLN-s.- Identifiability and Informative Experiments in Open and Closed-Loop Identification.- On Interpolation and the Kimura-Georgiou Parametrization.- The Control of Error in Numerical Methods.- Contour Reconstruction and Matching Using Recursive Smoothing Splines.- Role of LQ Decomposition in Subspace Identification Methods.- Canonical Operators on Graphs.
Modeling and Control of Antennas and Telescopes
Modeling and Control of Antennas and Telescopes presents the author’s research and field experience in the area of antenna modeling, dynamics, and control. The required spacecraft tracking accuracy of 1 mdeg was the impetus for the new approaches to the antenna controls that use model based controllers (LQG and H¥ ). Consequently, modeling also required a new approach using system identification techniques. Most of the material presented is new in the telescope industry. The methods have been not only analyzed and tested, but actually implemented, giving confidence in the final result, which is significantly increased antenna pointing accuracy.
Identification of nonlinear systems using neural networks and polynomial models : A block-oriented approach
The book gives a comparative study of their gradient approximation accuracy, computational complexity, and convergence rates and furthermore presents some new and original methods concerning the model parameter adjusting with gradient-based techniques. "Identification of Nonlinear Systems Using Neural Networks and Polynomal Models".
Functional Fractional Calculus for System Identification and Controls
In this book not only mathematical abstractions are discussed in a lucid manner, but also several practical applications are given particularly for system identification, description and then efficient controls.
Dynamic Modeling, Predictive Control and Performance Monitoring : A Data-driven Subspace Approach
A typical design procedure for model predictive control or control performance monitoring consists of: identification of a parametric or nonparametric model, derivation of the output predictor from the model and design of the control law or calculation of performance indices according to the predictor.
Digital Control Systems : Design, Identification and Implementation
Digital Control Systems demonstrates in detail how to design and implement high-performance model-based controllers combining system identification and control design techniques extensively tested in industrial milieux. The effective use of these techniques is illustrated in the context of various systems including: d.c. motors, flexible transmissions, air heaters, distillation columns and hot-dip galvanizing. Topics covered include: • essentials of computer-based control systems; • controller design methods (robust pole placement, long-range-predictive control, state space, digital PID, etc.); • system identification techniques; • practical aspects of system identification and digital control.
Control of Uncertain Systems : Modelling, Approximation, and Design; A Workshop on the Occasion of Keith Glover's 60th Birthday
This Festschrift contains a collection of articles by friends, co-authors, colleagues, and former Ph.D. students of Keith Glover, Professor of Engineering at the University of Cambridge, on the occasion of his sixtieth birthday. Professor Glover's scientific work spans a wide variety of topics, the main themes being system identification, model reduction and approximation, robust controller synthesis, and control of aircraft and engines. The articles in this volume are a tribute to Professor Glover's seminal work in these areas.
Cluster Analysis for Data Mining and System Identification
Presents new approaches to data mining and system identification, and new techniques and tools are presented for the clustering, classification, regression and visualization of complex datasets.
Blind Speech Separation
This is the first book to provide a cutting edge reference to the fascinating topic of blind source separation (BSS) for convolved speech mixtures. Through contributions by the foremost experts on the subject, the book provides an up-to-date account of research findings, explains the underlying theory, and discusses potential applications. The individual chapters are designed to be tutorial in nature with specific emphasis on an in-depth treatment of state of the art techniques. Blind Speech Separation is divided into three parts:Part 1 presents overdetermined or critically determined BSS. Here the main technology is independent component analysis (ICA). ICA is a statistical method for extracting mutually independent sources from their mixtures. Part 2 addresses underdetermined BSS, where there are fewer microphones than source signals. Here, the sparseness of speech sources is very useful; we can utilize time-frequency diversity, where sources are active in different regions of the time-frequency plane.Part 3 presents monaural BSS where there is only one microphone. Here, we can separate a mixture by using the harmonicity and temporal structure of the sources. We can build a probabilistic framework by assuming a source model, and separate a mixture by maximizing the a posteriori probability of the sources.
Blind Equalization and System Identification : Batch Processing Algorithms, Performance and Applications
Discrete-time signal processing has had a momentous impact on advances in engineering and science over recent decades. The rapid progress of digital and mixed-signal integrated circuits in processing speed, functionality and cost-effectiveness has led to their ubiquitous employment in signal processing and transmission in diverse milieux. Topics covered include: • SISO, MIMO and 2-d non-blind equalization (deconvolution) algorithms. • SISO, MIMO and 2-d blind equalization (deconvolution) algorithms. • SISO, MIMO and 2-d blind system identification algorithms. • algorithm analyses and improvements. • applications of SISO, MIMO and 2-d blind equalization/identification algorithms.
Adaptive Nonlinear System Identification : The Volterra and Wiener Model Approaches
Adaptive Nonlinear System Identification: The Volterra and Wiener Model Approaches introduces engineers and researchers to the field of nonlinear adaptive system identification. The book includes recent research results in the area of adaptive nonlinear system identification and presents simple, concise, easy-to-understand methods for identifying nonlinear systems. These methods use adaptive filter algorithms that are well known for linear systems identification. They are applicable for nonlinear systems that can be efficiently modeled by polynomials.After a brief introduction to nonlinear systems and to adaptive system identification, the author presents the discrete Volterra model approach. This is followed by an explanation of the Wiener model approach. Adaptive algorithms using both models are developed. The performance of the two methods are then compared to determine which model performs better for system identification applications.
Adaptive Learning of Polynomial Networks : Genetic Programming, Backpropagation and Bayesian Methods
This book provides theoretical and practical knowledge for develop ment of algorithms that infer linear and nonlinear models. It offers a methodology for inductive learning of polynomial neural network mod els from data. The design of such tools contributes to better statistical data modelling when addressing tasks from various areas like system identification, chaotic time-series prediction, financial forecasting and data mining. The main claim is that the model identification process involves several equally important steps: finding the model structure, estimating the model weight parameters, and tuning these weights with respect to the adopted assumptions about the underlying data distrib ution. When the learning process is organized according to these steps, performed together one after the other or separately, one may expect to discover models that generalize well.
Acoustic MIMO Signal Processing
Telecommunication systems and human-machine interfaces start employing multiple microphones and loudspeakers in order to make conversations and interactions more lifelike, hence more efficient. This development gives rise to a variety of acoustic signal processing problems under multiple-input multiple-output (MIMO) scenarios, encompassing distant speech acquisition, sound source localization and tracking, echo and noise control, source separation and speech dereverberation, and many others. The last decade has witnessed a growing interest in exploring these problems, but there has been little effort to develop a theory to have all these problems investigated in a unified framework. This unique book attempts to fill the gap.
