vidyasagar mathukumalli (23 Ergebnisse)

Hardcover. Zustand: Good. First Edition. With dust jacket. It's a preowned item in good condition and includes all the pages. It may have some general signs of wear and tear, such as markings, highlighting, slight damage to the cover, minimal wear to the binding, etc., but they will not affect the overall reading experience.

Hardcover. Zustand: Good. No Jacket. Pages can have notes/highlighting. Spine may show signs of wear. ~ ThriftBooks: Read More, Spend Less.

Zustand: Poor. This is an ex-library book and may have the usual library/used-book markings inside.This book has hardback covers. In poor condition, suitable as a reading copy. No dust jacket. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,650grams, ISBN:0136232809.

Zustand: Fair. This is an ex-library book and may have the usual library/used-book markings inside.This book has hardback covers. In fair condition, suitable as a study copy. No dust jacket. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,700grams, ISBN:0136232809.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Learning and Generalization provides a formal mathematical theory addressing intuitive questions of the type: - How does a machine learn a concept on the basis of examples - How can a neural network, after training, correctly predict the outcome of a previously unseen input - How much training is required to achieve a given level of accuracy in the prediction - How can one identify the dynamical behaviour of a nonlinear control system by observing its input-output behaviour over a finite time The second edition covers new areas including:- support vector machines;- fat-shattering dimensions and applications to neural network learning;- learning with dependent samples generated by a beta-mixing process;- connections between system identification and learning theory;- probabilistic solution of 'intractable problems' in robust control and matrix theory using randomized algorithms.It also contains solutions to some of the open problems posed in the first edition, while adding new open problems.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - System and Control theory is one of the most exciting areas of contemporary engineering mathematics. From the analysis of Watt's steam engine governor - which enabled the Industrial Revolution - to the design of controllers for consumer items, chemical plants and modern aircraft, the area has always drawn from a broad range of tools. It has provided many challenges and possibilities for interaction between engineering and established areas of 'pure' and 'applied' mathematics. This impressive volume collects a discussion of more than fifty open problems which touch upon a variety of subfields, including: chaotic observers, nonlinear local controlability, discrete event and hybrid systems, neural network learning, matrix inequalities, Lyapunov exponents, and many other issues. Proposed and explained by leading researchers, they are offered with the intention of generating further work, as well as inspiration for many other similar problems which may naturally arise from them. With extensive references, this book will be a useful reference source - as well as an excellent addendum to the textbooks in the area.

Zustand: Sehr gut. Zustand: Sehr gut | Seiten: 512 | Sprache: Englisch | Produktart: Bücher | Learning and Generalization provides a formal mathematical theory addressing intuitive questions of the type: ¿ How does a machine learn a concept on the basis of examples? ¿ How can a neural network, after training, correctly predict the outcome of a previously unseen input? ¿ How much training is required to achieve a given level of accuracy in the prediction? ¿ How can one identify the dynamical behaviour of a nonlinear control system by observing its input-output behaviour over a finite time? The second edition covers new areas including: ¿ support vector machines; ¿ fat-shattering dimensions and applications to neural network learning; ¿ learning with dependent samples generated by a beta-mixing process; ¿ connections between system identification and learning theory; ¿ probabilistic solution of 'intractable problems' in robust control and matrix theory using randomized algorithms. It also contains solutions to some of the open problems posed in the first edition, while adding new open problems.

Zustand: New.

Taschenbuch. Zustand: Neu. Neuware -This book introduces the so-called ''stable factorization approach'' to the synthesis of feedback controllers for linear control systems. The key to this approach is to view the multi-input, multi-output (MIMO) plant for which one wishes to design a controller as a matrix over the fraction field F associated with a commutative ring with identity, denoted by R, which also has no divisors of zero. In this setting, the set of single-input, single-output (SISO) stable control systems is precisely the ring R, while the set of stable MIMO control systems is the set of matrices whose elements all belong to R. The set of unstable, meaning not necessarily stable, control systems is then taken to be the field of fractions F associated with R in the SISO case, and the set of matrices with elements in F in the MIMO case. The central notion introduced in the book is that, in most situations of practical interest, every matrix P whose elements belong to F can be ''factored'' as a ''ratio'' of two matrices N,D whose elements belong to R, in such a way that N,D are coprime. In the familiar case where the ring R corresponds to the set of bounded-input, bounded-output (BIBO)-stable rational transfer functions, coprimeness is equivalent to two functions not having any common zeros in the closed right half-plane including infinity. However, the notion of coprimeness extends readily to discrete-time systems, distributed-parameter systems in both the continuous- as well as discrete-time domains, and to multi-dimensional systems. Thus the stable factorization approach enables one to capture all these situations within a common framework.The key result in the stable factorization approach is the parametrization of all controllers that stabilize a given plant. It is shown that the set of all stabilizing controllers can be parametrized by a single parameter R, whose elements all belong to R. Moreover, every transfer matrix in the closed-loop system is an affine function of the design parameter R. Thus problems of reliable stabilization, disturbance rejection, robust stabilization etc. can all be formulated in terms of choosing an appropriate R.This is a reprint of the book Control System Synthesis: A Factorization Approach originally published by M.I.T. Press in 1985.Table of Contents: Introduction / Proper Stable Rational Functions / Scalar Systems: An Introduction / Matrix Rings / StabilizationSpringer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 188 pp. Englisch.

Taschenbuch. Zustand: Neu. Neuware -This book introduces the so-called ''stable factorization approach'' to the synthesis of feedback controllers for linear control systems. The key to this approach is to view the multi-input, multi-output (MIMO) plant for which one wishes to design a controller as a matrix over the fraction field F associated with a commutative ring with identity, denoted by R, which also has no divisors of zero. In this setting, the set of single-input, single-output (SISO) stable control systems is precisely the ring R, while the set of stable MIMO control systems is the set of matrices whose elements all belong to R. The set of unstable, meaning not necessarily stable, control systems is then taken to be the field of fractions F associated with R in the SISO case, and the set of matrices with elements in F in the MIMO case. The central notion introduced in the book is that, in most situations of practical interest, every matrix P whose elements belong to F can be ''factored'' as a ''ratio'' of two matrices N,D whose elements belong to R, in such a way that N,D are coprime. In the familiar case where the ring R corresponds to the set of bounded-input, bounded-output (BIBO)-stable rational transfer functions, coprimeness is equivalent to two functions not having any common zeros in the closed right half-plane including infinity. However, the notion of coprimeness extends readily to discrete-time systems, distributed-parameter systems in both the continuous- as well as discrete-time domains, and to multi-dimensional systems. Thus the stable factorization approach enables one to capture all these situations within a common framework.The key result in the stable factorization approach is the parametrization of all controllers that stabilize a given plant. It is shown that the set of all stabilizing controllers can be parametrized by a single parameter R, whose elements all belong to R. Moreover, every transfer matrix in the closed-loop system is an affine function of the design parameter R. Thus problems of reliable stabilization, disturbance rejection, robust stabilization etc. can all be formulated in terms of choosing an appropriate R.This is a reprint of the book Control System Synthesis: A Factorization Approach originally published by M.I.T. Press in 1985.Table of Contents: Filtering and Sensitivity Minimization / Robustness / Extensions to General SettingsSpringer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 228 pp. Englisch.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book introduces the so-called ''stable factorization approach'' to the synthesis of feedback controllers for linear control systems. The key to this approach is to view the multi-input, multi-output (MIMO) plant for which one wishes to design a controller as a matrix over the fraction field F associated with a commutative ring with identity, denoted by R, which also has no divisors of zero. In this setting, the set of single-input, single-output (SISO) stable control systems is precisely the ring R, while the set of stable MIMO control systems is the set of matrices whose elements all belong to R. The set of unstable, meaning not necessarily stable, control systems is then taken to be the field of fractions F associated with R in the SISO case, and the set of matrices with elements in F in the MIMO case. The central notion introduced in the book is that, in most situations of practical interest, every matrix P whose elements belong to F can be ''factored'' as a ''ratio'' of two matrices N,D whose elements belong to R, in such a way that N,D are coprime. In the familiar case where the ring R corresponds to the set of bounded-input, bounded-output (BIBO)-stable rational transfer functions, coprimeness is equivalent to two functions not having any common zeros in the closed right half-plane including infinity. However, the notion of coprimeness extends readily to discrete-time systems, distributed-parameter systems in both the continuous- as well as discrete-time domains, and to multi-dimensional systems. Thus the stable factorization approach enables one to capture all these situations within a common framework.The key result in the stable factorization approach is the parametrization of all controllers that stabilize a given plant. It is shown that the set of all stabilizing controllers can be parametrized by a single parameter R, whose elements all belong to R. Moreover, every transfer matrix in the closed-loop system is an affine function of the design parameter R. Thus problems of reliable stabilization, disturbance rejection, robust stabilization etc. can all be formulated in terms of choosing an appropriate R.This is a reprint of the book Control System Synthesis: A Factorization Approach originally published by M.I.T. Press in 1985.Table of Contents: Introduction / Proper Stable Rational Functions / Scalar Systems: An Introduction / Matrix Rings / Stabilization.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book introduces the so-called ''stable factorization approach'' to the synthesis of feedback controllers for linear control systems. The key to this approach is to view the multi-input, multi-output (MIMO) plant for which one wishes to design a controller as a matrix over the fraction field F associated with a commutative ring with identity, denoted by R, which also has no divisors of zero. In this setting, the set of single-input, single-output (SISO) stable control systems is precisely the ring R, while the set of stable MIMO control systems is the set of matrices whose elements all belong to R. The set of unstable, meaning not necessarily stable, control systems is then taken to be the field of fractions F associated with R in the SISO case, and the set of matrices with elements in F in the MIMO case. The central notion introduced in the book is that, in most situations of practical interest, every matrix P whose elements belong to F can be ''factored'' as a ''ratio'' of two matrices N,D whose elements belong to R, in such a way that N,D are coprime. In the familiar case where the ring R corresponds to the set of bounded-input, bounded-output (BIBO)-stable rational transfer functions, coprimeness is equivalent to two functions not having any common zeros in the closed right half-plane including infinity. However, the notion of coprimeness extends readily to discrete-time systems, distributed-parameter systems in both the continuous- as well as discrete-time domains, and to multi-dimensional systems. Thus the stable factorization approach enables one to capture all these situations within a common framework.The key result in the stable factorization approach is the parametrization of all controllers that stabilize a given plant. It is shown that the set of all stabilizing controllers can be parametrized by a single parameter R, whose elements all belong to R. Moreover, every transfer matrix in the closed-loop system is an affine function of the design parameter R. Thus problems of reliable stabilization, disturbance rejection, robust stabilization etc. can all be formulated in terms of choosing an appropriate R.This is a reprint of the book Control System Synthesis: A Factorization Approach originally published by M.I.T. Press in 1985.Table of Contents: Filtering and Sensitivity Minimization / Robustness / Extensions to General Settings.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This brief introduces people with a basic background in probability theory to various problems in cancer biology that are amenable to analysis using methods of probability theory and statistics. The title mentions 'cancer biology' and the specific illustrative applications reference cancer data but the methods themselves are more broadly applicable to all aspects of computational biology. Aside from providing a self-contained introduction to basic biology and to cancer, the brief describes four specific problems in cancer biology that are amenable to the application of probability-based methods. The application of these methods is illustrated by applying each of them to actual data from the biology literature. After reading the brief, engineers and mathematicians should be able to collaborate fruitfully with their biologist colleagues on a wide variety of problems.

Taschenbuch. Zustand: Neu. Control Systems Synthesis | A Factorization Approach, Part I | Mathukumalli Vidyasagar | Taschenbuch | xvii | Englisch | 2011 | Springer International Publishing | EAN 9783031007002 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Taschenbuch. Zustand: Neu. Control Systems Synthesis | A Factorization Approach, Part II | Mathukumalli Vidyasagar | Taschenbuch | xviii | Englisch | 2011 | Springer International Publishing | EAN 9783031007019 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Zustand: Sehr gut. Zustand: Sehr gut | Seiten: 401 | Sprache: Englisch | Produktart: Bücher | Keine Beschreibung verfügbar.

Paperback. Zustand: Brand New. 2012 edition. 92 pages. 8.90x0.40x6.10 inches. In Stock.

Zustand: Good. Your purchase helps support Sri Lankan Children's Charity 'The Rainbow Centre'. Ex-library, so some stamps and wear, but in good overall condition. Our donations to The Rainbow Centre have helped provide an education and a safe haven to hundreds of children who live in appalling conditions.

Hardcover. Zustand: Fine. 1st Edition. 289 Pages.

Zustand: Sehr gut. Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher | Keine Beschreibung verfügbar.

Buch. Zustand: Neu. Neuware -Learning and Generalization provides a formal mathematical theory addressing intuitive questions of the type:¿ How does a machine learn a concept on the basis of examples ¿ How can a neural network, after training, correctly predict the outcome of a previously unseen input ¿ How much training is required to achieve a given level of accuracy in the prediction ¿ How can one identify the dynamical behaviour of a nonlinear control system by observing its input-output behaviour over a finite time The second edition covers new areas including:¿ support vector machines;¿ fat-shattering dimensions and applications to neural network learning;¿ learning with dependent samples generated by a beta-mixing process;¿ connections between system identification and learning theory;¿ probabilistic solution of 'intractable problems' in robust control and matrix theory using randomized algorithms.It also contains solutions to some of the open problems posed in the first edition, while adding new open problems.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 512 pp. Englisch.

Zustand: Good. Your purchase helps support Sri Lankan Children's Charity 'The Rainbow Centre'. Ex-library, so some stamps and wear, but in good overall condition. Our donations to The Rainbow Centre have helped provide an education and a safe haven to hundreds of children who live in appalling conditions.

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Learning and Generalization provides a formal mathematical theory addressing intuitive questions of the type: - How does a machine learn a concept on the basis of examples - How can a neural network, after training, correctly predict the outcome of a previously unseen input - How much training is required to achieve a given level of accuracy in the prediction - How can one identify the dynamical behaviour of a nonlinear control system by observing its input-output behaviour over a finite time The second edition covers new areas including:- support vector machines;- fat-shattering dimensions and applications to neural network learning;- learning with dependent samples generated by a beta-mixing process;- connections between system identification and learning theory;- probabilistic solution of 'intractable problems' in robust control and matrix theory using randomized algorithms.It also contains solutions to some of the open problems posed in the first edition, while adding new open problems.