Errors-in-Variables Methods in System Identification, S?derstr?m
Автор: Abonyi JГЎnos, Feil BalГЎzs Название: Cluster Analysis for Data Mining and System Identification ISBN: 3764379871 ISBN-13(EAN): 9783764379872 Издательство: Springer Рейтинг: Цена: 13969.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book presents new approaches to data mining and system identification. Algorithmsthat can be used for the clustering of data have been overviewed. New techniques andtools are presented for the clustering, classification, regression and visualization ofcomplex datasets. Special attention is given to the analysis of historical process data,tailored algorithms are presented for the data driven modeling of dynamical systems,determining the model order of nonlinear input-output black box models, and thesegmentation of multivariate time-series. The main methods and techniques areillustrated through several simulated and real-world applications from data mining andprocess engineering practice.The books is aimed primarily at practitioners, researches, and professionals in statistics,data mining, business intelligence, and systems engineering, but it is also accessible tograduate and undergraduate students in applied mathematics, computer science, electricaland process engineering. Familiarity with the basics of system identification and fuzzysystems is helpful but not required.
Автор: Tohru Katayama Название: Subspace Methods for System Identification ISBN: 1849969884 ISBN-13(EAN): 9781849969888 Издательство: Springer Рейтинг: Цена: 23508.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: An in-depth introduction to subspace methods for system identification in discrete-time linear systems thoroughly augmented with advanced and novel results, this text is structured into three parts.Part I deals with the mathematical preliminaries: numerical linear algebra;
This work is motivated by the ongoing open question of how information in the outside world is represented and processed by the brain. Consequently, several novel methods are developed.
A new mathematical formulation is proposed for the encoding and decoding of analog signals using integrate-and-fire neuron models. Based on this formulation, a novel algorithm, significantly faster than the state-of-the-art method, is proposed for reconstructing the input of the neuron.
Two new identification methods are proposed for neural circuits comprising a filter in series with a spiking neuron model. These methods reduce the number of assumptions made by the state-of-the-art identification framework, allowing for a wider range of models of sensory processing circuits to be inferred directly from input-output observations.
A third contribution is an algorithm that computes the spike time sequence generated by an integrate-and-fire neuron model in response to the output of a linear filter, given the input of the filter encoded with the same neuron model.
Описание: Algebraic Identification and Estimation Methods in Feedback Control Systems presents a model-based algebraic approach to online parameter and state estimation in uncertain dynamic feedback control systems.
Автор: M. Milanese; J. Norton; H. Piet-Lahanier; ?. Walte Название: Bounding Approaches to System Identification ISBN: 0306450216 ISBN-13(EAN): 9780306450211 Издательство: Springer Рейтинг: Цена: 36197.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Describes advances in techniques and applications of bounding of the parameters, or state variables, of uncertain dynamical systems. This title explores the application of the bounding approach as an alternative to the probabilistic analysis of such systems, relating its importance to robust control-system design.
Автор: Oliver Nelles Название: Nonlinear System Identification ISBN: 3642086748 ISBN-13(EAN): 9783642086748 Издательство: Springer Рейтинг: Цена: 15672.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The goal of this book is to provide engineers and scientIsts in academia and industry with a thorough understanding of the underlying principles of nonlinear system identification. The reader will be able to apply the discussed models and methods to real problems with the necessary confidence and the awareness of potential difficulties that may arise in practice. This book is self-contained in the sense that it requires merely basic knowledge of matrix algebra, signals and systems, and statistics. Therefore, it also serves as an introduction to linear system identification and gives a practical overview on the major optimization methods used in engineering. The emphasis of this book is on an intuitive understanding of the subject and the practical application of the discussed techniques. It is not written in a theorem/proof style; rather the mathematics is kept to a minimum and the pursued ideas are illustrated by numerous figures, examples, and real-world applications. Fifteen years ago, nonlinear system identification was a field of several ad-hoc approaches, each applicable only to a very restricted class of systems. With the advent of neural networks, fuzzy models, and modern structure opti- mization techniques a much wider class of systems can be handled. Although one major characteristic of nonlinear systems is that almost every nonlinear system is unique, tools have been developed that allow the use of the same ap- proach for a broad variety of systems.
Автор: Torsten Bohlin Название: Interactive System Identification: Prospects and Pitfalls ISBN: 3642486207 ISBN-13(EAN): 9783642486203 Издательство: Springer Рейтинг: Цена: 6986.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The craft of designing mathematical models of dynamic objects offers a large number of methods to solve subproblems in the design, typically parameter estimation, order determination, validation, model reduc- tion, analysis of identifiability, sensi tivi ty and accuracy.
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