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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...

Nonlinear Modeling: Advanced Black-Box Techniques discusses methods on

• Neural nets and related model structures for nonlinear system identification;
• Enhanced multi-stream Kalman filter training for recurrent networks;
• The support vector method of function estimation;
• Parametric density estimation for the classification of acoustic feature vectors in speech recognition;
• Wavelet-based modeling of nonlinear systems;
• Nonlinear identification based on fuzzy models;
• Statistical learning in control and matrix theory;
...

Nonlinear Modeling: Advanced Black-Box Techniques discusses methods on

• Neural nets and related model structures for nonlinear system identification;
• Enhanced multi-stream Kalman filter training for recurrent networks;
• The support vector method of function estimation;
• Parametric density estimation for the classification of acoustic feature vectors in speech recognition;
• Wavelet-based modeling of nonlinear systems;
• Nonlinear identification based on fuzzy models;
• Statistical learning in control and matrix theory;
...

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...

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
...

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...

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...