Fuzzy logic and neural networks have been discussed in detail through illustrative examples, methods and generic applications. Extensive and carefully selected references is an invaluable resource for further study of fuzzy logic and neural networks. Each chapter is followed by a question bank which is intended to help in the preparation for external examination.

Artificial neural networks can mimic the biological information-processing mechanism in - a very limited sense. Fuzzy logic provides a basis for representing uncertain and imprecise knowledge and forms a basis for human reasoning. Neural networks display genuine promise in solving problems, but a definitive theoretical basis does not yet exist for their design. Fusion of Neural Networks, Fuzzy Systems and Genetic Algorithms integrates neural net, fuzzy system, and evolutionary computing in system design that enables its readers to handle complexity

This book systematically synthesizes research achievements in the field of fuzzy neural networks in recent years. It also provides a comprehensive presentation of the developments in fuzzy neural networks, with regard to theory as well as their application to system modeling and image restoration. Special emphasis is placed on the fundamental concepts and architecture analysis of fuzzy neural networks. The book is unique in treating all kinds of fuzzy neural networks and their learning algorithms and universal approximations, and employing simulation examples which are carefully designed to help the reader grasp the underlying theory.

Neural Networks Theory is a major contribution to the neural networks literature. It is a treasure trove that should be mined by the thousands of researchers and practitioners worldwide who have not previously had access to the fruits of Soviet and Russian neural network research. Dr. Galushkin is to be congratulated and thanked for his completion of this monumental work; a book that only he could write. It is a major gift to the world."
Robert Hecht Nielsen, Computational Neurobiology, University of California, San Diego

The book is a collection of selected papers from the 18th WIRN workshop, the annual meeting of the Italian Neural Networks Society (SIREN). As the number 18 marks the year young people come of age in Italy, the society invited two generations of researchers to participate in a discussion on neural networks: those new to the field and those with extensive familiarity with the neural paradigm.

Fuzzy Logic, at present is a hot topic, among academicians as well various programmers. This book is provided to give a broad, in-depth overview of the field of Fuzzy Logic. The basic principles of Fuzzy Logic are discussed in detail with various solved examples. The different approaches and solutions to the problems given in the book are well balanced and pertinent to the Fuzzy Logic research projects. The applications of Fuzzy Logic are also dealt to make the readers understand the concept of Fuzzy Logic. The solutions to the problems are programmed using MATLAB 6.0 and the simulated results are given. The MATLAB Fuzzy Logic toolbox is provided for easy reference.

The development of neural networks has now reached the stage where they are employed in a large variety of practical contexts. However, to date the majority of such implementations have been in software. While it is generally recognised that hardware implementations could, through performance advantages, greatly increase the use of neural networks, to date the relatively high cost of developing Application-Specific Integrated Circuits (ASICs) has meant that only a small number of hardware neurocomputers has gone beyond the research-prototype stage.

Introduction to Neural Networks with Java, Second Edition, introduces the Java programmer to the world of Neural Networks and Artificial Intelligence. Neural network architectures, such as the feedforward, Hopfield, and self-organizing map architectures are discussed. Training techniques, such as backpropagation, genetic algorithms and simulated annealing are also introduced. Practical examples are given for each neural network. Examples include the traveling salesman problem, handwriting recognition, financial prediction, game strategy, mathematical functions, and Internet bots. All Java source code is available online for easy downloading.

Artificial neural networks are used to model systems that receive inputs and produce outputs. The relationships between the inputs and outputs and the representation parameters are critical issues in the design of related engineering systems, and sensitivity analysis concerns methods for analyzing these relationships. Perturbations of neural networks are caused by machine imprecision, and they can be simulated by embedding disturbances in the original inputs or connection weights, allowing us to study the characteristics of a function under small perturbations of its parameters.

Introduction to Neural Networks with C#, Second Edition, introduces the C# programmer to the world of Neural Networks and Artificial Intelligence. Neural network architectures, such as the feedforward, Hopfield, and self-organizing map architectures are discussed. Training techniques, such as backpropagation, genetic algorithms and simulated annealing are also introduced. Practical examples are given for each neural network. Examples include the traveling salesman problem, handwriting recognition, financial prediction, game strategy, mathematical functions, and Internet bots. All C# source code is available online for easy downloading.

A number of publications have explored the use of genetic algorithms as a tool for designing fuzzy systems. This volume explores and discusses this symbiosis of evolutionary computation and fuzzy logic. The book summarizes and analyzes the field of genetic fuzzy systems, paying special attention to genetic algorithms that adapt and learn the knowledge base of a fuzzy-rule-based system. It introduces the general concepts, foundations and design principles of genetic fuzzy systems and covers the topic of genetic tuning of fuzzy systems. It also introduces the three fundamental approaches to genetic learning processes in fuzzy systems: the Michigan, Pittsburgh and Iterative-learning methods.

A neural network refers to interconnecting artificial neurons that mimic the properties of biological neurons to perform sophisticated, intelligent tasks. This authoritative reference offers a comprehensive understanding of the underpinnings and practical applications of artificial neural networks and their use in the retrieval of geophysical parameters. Professionals find expert guidance on the development and evaluation of neural network algorithms that process data from a new generation of hyperspectral sensors. Engineers discover how to use neural networks to approximate remote sensing inverse functions with emphasis on model selection, preprocessing, initialization, training, and performance evaluation.

Process Neural Network: Theory and Applications" proposes the concept and model of a process neural network for the first time, showing how it expands the mapping relationship between the input and output of traditional neural networks and enhances the expression capability for practical problems, with broad applicability to solving problems relating to processes in practice. Some theoretical problems such as continuity, functional approximation capability, and computing capability, are closely examined. The application methods, network construction principles, and optimization algorithms of process neural networks in practical fields, such as nonlinear time-varying system modeling, process signal pattern recognition, dynamic system identification, and process forecast, are discussed in detail. The information processing flow and the mapping relationship between inputs and outputs of process neural networks are richly illustrated.

Recent research indicates that complex-valued neural networks whose parameters (weights and threshold values) are all complex numbers are in fact useful, containing characteristics bringing about many significant applications.
Complex-Valued Neural Networks: Utilizing High-Dimensional Parameters covers the current state-of-the-art theories and applications of neural networks with high-dimensional parameters such as complex-valued neural networks, quantum neural networks, quaternary neural networks, and Clifford neural networks, which have been developing in recent years.

Modern aerospace, automotive, nautical, industrial, microsystem-assembly and robotic systems are becoming more and more complex. High-performance vehicles no longer have built-in error safety margins, but are inherently unstable by design to allow for more flexible maneuvering options. With the push towards better performance in terms of greater accuracy and faster speed of response, control demands are increasing. The combination of highly nonlinear dynamics, relaxed static stability, and tight performance specifications places increasing demands on the design of feedback systems for control

The book shows a very original organization addressing in a non traditional way, but with a systematic approach, to who has an interest in using mathematics in the social sciences.

Using theory as a foundation for practical programming, this text presents the theory of logic programming with clear proofs, extended examples, and implementation techniques. It covers logical theory, practical programming, and the structure of a simple Prolog implementation.

This volume contains the refereed proceedings of the 11th International Conference on Logic Programming and Nonmonotonic Reasoning, LPNMR 2011, held in May 2011 in Vancouver, Canada.

A comprehensive introduction to fuzzy logic, this book leads the reader through the complete process of designing, constructing, implementing, verifying and maintaining a platform-independent fuzzy system model. It is written in a tutorial style that assumes no background in fuzzy logic on the reader's part. The enclosed disk contains all of the book's examples in C++ code.