Articulates a series of scientific and technological developments described in a didactic way Shows a broad view of biometrics in several contexts: Current cases of study in five different areas Provides experimental results in different environments (availability of data and computer code to reproduce the experiments) Includes transdisciplinary approaches and discussions for each chapter Innovation as part of the final cases of study: developments are already affecting the new businesses of the digital age and the era of the brain

Metaheuristic optimization has become a prime alternative for solving complex optimization problems in several areas. Hence, practitioners and researchers have been paying extensive attention to those metaheuristic algorithms that are mainly based on natural phenomena. However, when those algorithms are implemented, there are not enough books that deal with theoretical and experimental problems in a friendly manner so this book presents a novel structure that includes a complete description of the most important metaheuristic optimization algorithms as well as a new proposal of a new metaheuristic optimization named earthquake optimization. This book also has several practical exercises and a toolbox for MATLAB (R) and a toolkit for LabVIEW are integrated as complementary material for this book. These toolkits allow readers to move from a simulation environment to an experimentation one very fast. This book is suitable for researchers, students, and professionals in several areas, such as economics, architecture, computer science, electrical engineering, and control systems. The unique features of this book are as follows: Developed for researchers, undergraduate and graduate students, and practitioners A friendly description of the main metaheuristic optimization algorithms Theoretical and practical optimization examples A new earthquake optimization algorithm Updated state-of-the-art and research optimization projects The authors are multidisciplinary/interdisciplinary lecturers and researchers who have written a structure-friendly learning methodology to understand each metaheuristic optimization algorithm presented in this book.

Metaheuristic optimization has become a prime alternative for solving complex optimization problems in several areas. Hence, practitioners and researchers have been paying extensive attention to those metaheuristic algorithms that are mainly based on natural phenomena. However, when those algorithms are implemented, there are not enough books that deal with theoretical and experimental problems in a friendly manner so this book presents a novel structure that includes a complete description of the most important metaheuristic optimization algorithms as well as a new proposal of a new metaheuristic optimization named earthquake optimization. This book also has several practical exercises and a toolbox for MATLAB (R) and a toolkit for LabVIEW are integrated as complementary material for this book. These toolkits allow readers to move from a simulation environment to an experimentation one very fast. This book is suitable for researchers, students, and professionals in several areas, such as economics, architecture, computer science, electrical engineering, and control systems. The unique features of this book are as follows: Developed for researchers, undergraduate and graduate students, and practitioners A friendly description of the main metaheuristic optimization algorithms Theoretical and practical optimization examples A new earthquake optimization algorithm Updated state-of-the-art and research optimization projects The authors are multidisciplinary/interdisciplinary lecturers and researchers who have written a structure-friendly learning methodology to understand each metaheuristic optimization algorithm presented in this book.

Intelligent Control with LabVIEW (TM) is a fresh and pragmatic approach to the understanding of a subject often clouded by too much mathematical theory. It exploits the full suite of tools provided by LabVIEW (TM), showing the student how to design, develop, analyze, and visualize intelligent control algorithms quickly and simply. Block diagrams are used to follow the progress of an algorithm through the design process and allow seamless integration with hardware systems for rapid deployment in laboratory experiments. This text delivers a thorough grounding in the main tools of intelligent control: fuzzy logic systems; artificial neural networks; neuro-fuzzy systems; evolutionary methods; and predictive methods. Learning and teaching are facilitated by: extensive use of worked examples; end of chapter problems with separate solutions; and provision of intelligent control tools for LabVIEW (TM).

This monograph describes the synthesis and use of biologically-inspired artificial hydrocarbon networks (AHNs) for approximation models associated with machine learning and a novel computational algorithm with which to exploit them. The reader is first introduced to various kinds of algorithms designed to deal with approximation problems and then, via some conventional ideas of organic chemistry, to the creation and characterization of artificial organic networks and AHNs in particular.

The advantages of using organic networks are discussed with the rules to be followed to adapt the network to its objectives. Graph theory is used as the basis of the necessary formalism. Simulated and experimental examples of the use of fuzzy logic and genetic algorithms with organic neural networks are presented and a number of modeling problems suitable for treatment by AHNs are described:

. approximation;

. inference;

. clustering;

. control;

. classification; and

. audio-signal filtering.

The text finishes with a consideration of directions in which AHNs could be implemented and developed in future. A complete LabVIEW toolkit, downloadable from the book s page at springer.com enables readers to design and implement organic neural networks of their own.

The novel approach to creating networks suitable for machine learning systems demonstrated in "Artificial Organic Networks" will be of interest to academic researchers and graduate students working in areas associated with computational intelligence, intelligent control, systems approximation and complex networks."

Intelligent control is a rapidly developing, complex, and challenging field of increasing practical importance and still greater potential. Its applications have a solid core in robotics and mechatronics but branch out into areas as diverse as process control, automotive industry, medical equipment, renewable energy and air conditioning.

"Intelligent Control with LabVIEW " is a fresh and pragmatic approach to the understanding of a subject often clouded by too much mathematical theory. It exploits the full suite of tools provided by LabVIEW, showing the student how to design, develop, analyze, and visualize intelligent control algorithms quickly and simply. Block diagrams are used to follow the progress of an algorithm through the design process and allow seamless integration with hardware systems for rapid deployment in laboratory experiments.

This text delivers a thorough grounding in the main tools of intelligent control:

fuzzy logic systems, emulating human reasoning processes;

artificial neural networks, mimicking our ability to learn;

neuro-fuzzy systems for implementing controller adaptation;

evolutionary methods, using the ideas of genetic evolution for optimization; and

predictive methods, giving information about a system s future behavior.

"Intelligent Control with LabVIEW " facilitates the learning and teaching of its subject by:

extensive use of worked examples;

end-of-chapter problems with separate solutions; and

provision of intelligent control tools for LabVIEW .

Graduate and senior undergraduate students learning about intelligent control will find great help in this textbook s easy-to-read style and graphical programming approach. The book will be of material assistance to instructors interested in using LabVIEW -based teaching when designing a course in intelligent control. Researchers in intelligent systems who employ LabVIEW as a laboratory tool will also benefit from its dedicated support for intelligent control.