Johan A.K. Suykens's Books

Artificial Neural Networks for Modelling and Control of Non-Linear Systems

By: Johan A.K. Suykens,Joos P.L. Vandewalle,B.L. de Moor

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Artificial Neural Networks for Modelling and Control of Non-Linear Systems

By: Johan A.K. Suykens,Joos P.L. Vandewalle,B.L. de Moor

Artificial neural networks possess several properties that make them particularly attractive for applications to modelling and control of complex non-linear systems. Among these properties are their universal approximation ability, their parallel network structure and the availability of on- and off-line learning methods for the interconnection weights. However, dynamic models that contain neural network architectures might be highly non-linear and difficult to analyse as a result. Artificial Neural Networks for Modelling and Control of Non-Linear Systems investigates the subject from a system theoretical point of view. However the mathematical theory that is required from the reader is limited to matrix calculus, basic analysis, differential equations and basic linear system theory. No preliminary knowledge of neural networks is explicitly required. The book presents both classical and novel network architectures and learning algorithms for modelling and control. Topics include non-linear system identification, neural optimal control, top-down model based neural control design and stability analysis of neural control systems. A major contribution of this book is to introduce NLq Theory as an extension towards modern control theory, in order to analyze and synthesize non-linear systems that contain linear together with static non-linear operators that satisfy a sector condition: neural state space control systems are an example. Moreover, it turns out that NLq Theory is unifying with respect to many problems arising in neural networks, systems and control. Examples show that complex non-linear systems can be modelled and controlled within NLq theory, including mastering chaos. The didactic flavor of this book makes it suitable for use as a text for a course on Neural Networks. In addition, researchers and designers will find many important new techniques, in particular NLq emTheory, that have applications in control theory, system theory, circuit theory and Time Series Analysis.

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Page Count

242

Publisher

Springer Science & Business Media

Published Date

ISBN 10

1475724934

ISBN 13

9781475724936

Least Squares Support Vector Machines

By: Johan A K Suykens,Tony Van Gestel,Joseph De Brabanter,Bart De Moor,Joos P L Vandewalle

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Least Squares Support Vector Machines

By: Johan A K Suykens,Tony Van Gestel,Joseph De Brabanter,Bart De Moor,Joos P L Vandewalle

This book focuses on Least Squares Support Vector Machines (LS-SVMs) which are reformulations to standard SVMs. LS-SVMs are closely related to regularization networks and Gaussian processes but additionally emphasize and exploit primal-dual interpretations from optimization theory. The authors explain the natural links between LS-SVM classifiers and kernel Fisher discriminant analysis. Bayesian inference of LS-SVM models is discussed, together with methods for imposing sparseness and employing robust statistics.The framework is further extended towards unsupervised learning by considering PCA analysis and its kernel version as a one-class modelling problem. This leads to new primal-dual support vector machine formulations for kernel PCA and kernel CCA analysis. Furthermore, LS-SVM formulations are given for recurrent networks and control. In general, support vector machines may pose heavy computational challenges for large data sets. For this purpose, a method of fixed size LS-SVM is proposed where the estimation is done in the primal space in relation to a Nyström sampling with active selection of support vectors. The methods are illustrated with several examples.

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Page Count

310

Publisher

World Scientific

Published Date

ISBN 10

9814487597

ISBN 13

9789814487597

Cellular Neural Networks, Multi-scroll Chaos And Synchronization

By: Johan A K Suykens,Mustak E Yalcin,Joos P L Vandewalle

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Cellular Neural Networks, Multi-scroll Chaos And Synchronization

By: Johan A K Suykens,Mustak E Yalcin,Joos P L Vandewalle

For engineering applications that are based on nonlinear phenomena, novel information processing systems require new methodologies and design principles. This perspective is the basis of the three cornerstones of this book: cellular neural networks, chaos and synchronization. Cellular neural networks and their universal machine implementations offer a well-established platform for processing spatial-temporal patterns and wave computing. Multi-scroll circuits are generalizations to the original Chua's circuit, leading to chip implementable circuits with increasingly complex attractors. Several applications make use of synchronization techniques for nonlinear systems. A systematic overview is given for Lur'e representable systems with global synchronization criteria for master-slave and mutual synchronization, robust synchronization, H∞ synchronization, time-delayed systems and impulsive synchronization.

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Page Count

247

Publisher

World Scientific

Published Date

ISBN 10

9814480959

ISBN 13

9789814480956

Least Squares Support Vector Machines

By: Johan A. K. Suykens,Tony Van Gestel,Jos De Brabanter

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Least Squares Support Vector Machines

By: Johan A. K. Suykens,Tony Van Gestel,Jos De Brabanter

This book focuses on Least Squares Support Vector Machines (LS-SVMs) which are reformulations to standard SVMs. LS-SVMs are closely related to regularization networks and Gaussian processes but additionally emphasize and exploit primal-dual interpretations from optimization theory. The authors explain the natural links between LS-SVM classifiers and kernel Fisher discriminant analysis. Bayesian inference of LS-SVM models is discussed, together with methods for imposing spareness and employing robust statistics. The framework is further extended towards unsupervised learning by considering PCA analysis and its kernel version as a one-class modelling problem. This leads to new primal-dual support vector machine formulations for kernel PCA and kernel CCA analysis. Furthermore, LS-SVM formulations are given for recurrent networks and control. In general, support vector machines may pose heavy computational challenges for large data sets. For this purpose, a method of fixed size LS-SVM is proposed where the estimation is done in the primal space in relation to a Nystrom sampling with active selection of support vectors. The methods are illustrated with several examples.

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Page Count

318

Publisher

World Scientific

Published Date

ISBN 10

9812381511

ISBN 13

9789812381514

Regularization, Optimization, Kernels, and Support Vector Machines

By: Johan A. K. Suykens,Marco Signoretto,Andreas Argyriou

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Regularization, Optimization, Kernels, and Support Vector Machines

By: Johan A. K. Suykens,Marco Signoretto,Andreas Argyriou

Regularization, Optimization, Kernels, and Support Vector Machines offers a snapshot of the current state of the art of large-scale machine learning, providing a single multidisciplinary source for the latest research and advances in regularization, sparsity, compressed sensing, convex and large-scale optimization, kernel methods, and support vector machines. Consisting of 21 chapters authored by leading researchers in machine learning, this comprehensive reference: Covers the relationship between support vector machines (SVMs) and the Lasso Discusses multi-layer SVMs Explores nonparametric feature selection, basis pursuit methods, and robust compressive sensing Describes graph-based regularization methods for single- and multi-task learning Considers regularized methods for dictionary learning and portfolio selection Addresses non-negative matrix factorization Examines low-rank matrix and tensor-based models Presents advanced kernel methods for batch and online machine learning, system identification, domain adaptation, and image processing Tackles large-scale algorithms including conditional gradient methods, (non-convex) proximal techniques, and stochastic gradient descent Regularization, Optimization, Kernels, and Support Vector Machines is ideal for researchers in machine learning, pattern recognition, data mining, signal processing, statistical learning, and related areas.

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Page Count

525

Publisher

CRC Press

Published Date

ISBN 10

0367658984

ISBN 13

9780367658984

Artificial Neural Networks for Modelling and Control of Non-Linear Systems

By: Johan A.K. Suykens,Joos P.L. Vandewalle,B.L. de Moor

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Artificial Neural Networks for Modelling and Control of Non-Linear Systems

By: Johan A.K. Suykens,Joos P.L. Vandewalle,B.L. de Moor

Average ratings

N/A

Write a review

Solve jigsaw puzzle

Age

Page Count

242

Publisher

Springer Science & Business Media

Published Date

ISBN 10

1475724934

ISBN 13

9781475724936

Book's by Johan A.K. Suykens

Artificial Neural Networks for Modelling and Control of Non-Linear Systems

Artificial Neural Networks for Modelling and Control of Non-Linear Systems

Least Squares Support Vector Machines

Least Squares Support Vector Machines

Cellular Neural Networks, Multi-scroll Chaos And Synchronization

Cellular Neural Networks, Multi-scroll Chaos And Synchronization

Least Squares Support Vector Machines

Least Squares Support Vector Machines

Regularization, Optimization, Kernels, and Support Vector Machines

Regularization, Optimization, Kernels, and Support Vector Machines

Artificial Neural Networks for Modelling and Control of Non-Linear Systems

Artificial Neural Networks for Modelling and Control of Non-Linear Systems

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