Since the birth of the Chua circuit in 1983, a considerable number of fruitful, fascinating and relevant research topics have arisen. In honor of the 25th anniversary of the invention of Chua's circuit, this book presents the 25 years of research on the implementation of Chua's circuit, and also discusses future directions and emerging applications of recent results.

The purpose of the book is to provide researchers, PhD students, and undergraduate students a research monograph containing both fundamentals on the topics and advanced results that have been recently obtained. With about 60 illustrations included in the book, it also shows the detailed schematics of several different implementations that can be easily reproduced with a low-cost experimental setup and PC-based measurement instrumentation.

Soft sensors are inferential estimators, drawing conclusions from process observations when hardware sensors are unavailable or unsuitable; they have an important auxiliary role in sensor validation when performance declines through senescence or fault accumulation.

The non-linear behaviour exhibited by many industrial processes can be usefully modelled with the techniques of computational intelligence: neural networks; fuzzy systems and nonlinear partial least squares.

Soft Sensors for Monitoring and Control of Industrial Processes underlines the real usefulness of each approach and the sensitivity of the individual steps in soft-sensor design to the choice of one or the other. Design paths are suggested and readers shown how to evaluate the effects of their choices. All the case studies reported, resulting from collaborations between the authors and a number of industrial partners, raised challenging soft-sensor-design problems. The applications of soft sensors presented in this volume are designed to cope with the whole range from measuring system backup and what-if analysis through real-time prediction for plant control to sensor diagnosis and validation. Some of the soft sensors developed here are implemented on-line at industrial plants.

Features:

• soft-sensor design;
• advice on data selection and choice of model structure;
• model validation;
• strategies for the improvement of soft-sensor performance;
• uses of soft sensors in fault detection and sensor validation;
• soft sensors in use in industrial applications such as a debutanizer column and a sulfur recovery unit.

This monograph guides interested readers researchers, graduate students and industrial process technologists through the design of their own soft sensors. It is self-contained with full references and appraisal of existing literature and data sets for some of the case studies can be downloaded from springer.com."

Soft sensors are inferential estimators, drawing conclusions from process observations when hardware sensors are unavailable or unsuitable; they have an important auxiliary role in sensor validation when performance declines through senescence or fault accumulation.

The non-linear behaviour exhibited by many industrial processes can be usefully modelled with the techniques of computational intelligence: neural networks; fuzzy systems and nonlinear partial least squares.

Soft Sensors for Monitoring and Control of Industrial Processes underlines the real usefulness of each approach and the sensitivity of the individual steps in soft-sensor design to the choice of one or the other. Design paths are suggested and readers shown how to evaluate the effects of their choices. All the case studies reported, resulting from collaborations between the authors and a number of industrial partners, raised challenging soft-sensor-design problems. The applications of soft sensors presented in this volume are designed to cope with the whole range from measuring system backup and what-if analysis through real-time prediction for plant control to sensor diagnosis and validation. Some of the soft sensors developed here are implemented on-line at industrial plants.

Features:

• soft-sensor design;
• advice on data selection and choice of model structure;
• model validation;
• strategies for the improvement of soft-sensor performance;
• uses of soft sensors in fault detection and sensor validation;
• soft sensors in use in industrial applications such as a debutanizer column and a sulfur recovery unit.

This monograph guides interested readers a"researchers, graduate students and industrial process technologists a" through the design of their own soft sensors. It is self-contained with full references and appraisal of existing literature and data sets for some of the case studies can be downloaded from springer.com.

In the last decade new artificial intelligence methods for the modelling and control of complex systems, namely neural networks, fuzzy logic and probabilistic reasoning have drawn the interest of researchers and engineers. Recently, the advantages achievable by using combinations of these methods, which have independent origin and evolution, have been pointed out, generating a new paradigm which is now termed "soft-computing". This new methodology subsumes the capabilities of neural networks for modelling non-linear systems and for solving classification problems, the power of fuzzy-logic to represent approximate or heuristic reasoning and the large capabilities of evolutionary computation for problem optimisation. The book presents a clear understanding of a new type of computation system, the cellular neural network (CNN), which has been successfully applied to the solution of many heavy computation problems, mainly in the fields of image processing and complex partial differential equations. CNNs' computation-based systems represent new opportunities for improving the soft-computation toolbox. The application of soft computing to complex systems and in particular to chaotic systems with the generation of chaotic dynamics by using CNN is also described. These aspects are of particular interest owing to their growing interest for research and application purposes.Specific topics covered in the text include:- fuzzy logic, control and neural networks;- artificial neural networks and their application in the modelling and control of dynamical systems;- evolutionary optimisation algorithms;- complex dynamics and cellular neural networks;- applications in urban traffic noise monitoring, robot control and rapid thermal process systems.

In this monograph, new structures of neural networks in multidimensional domains are introduced. These architectures are a generalization of the Multi-layer Perceptron (MLP) in Complex, Vectorial and Hypercomplex algebra. The approximation capabilities of these networks and their learning algorithms are discussed in a multidimensional context. The work includes the theoretical basis to address the properties of such structures and the advantages introduced in system modelling, function approximation and control. Some applications, referring to attractive themes in system engineering and a MATLAB software tool, are also reported. The appropriate background for this text is a knowledge of neural networks fundamentals. The manuscript is intended as a research report, but a great effort has been performed to make the subject comprehensible to graduate students in computer engineering, control engineering, computer sciences and related disciplines.