This is the first practical guide that enables you to actually work with artificial neural networks on your personal computer. It provides basic information on neural networks, as well as the following special features:source code listings in C**actual case studies in a wide range of applications, including radar signal detection, stock market prediction, musical composition, ship pattern recognition, and biopotential waveform classification**CASE tools for neural networks and hybrid expert system/neural networks**practical hints and suggestions on when and how to use neural network tools to solve real-world problems.

In this book, highly qualified multidisciplinary scientists grasp their recent researches motivated by the importance of artificial neural networks. It addresses advanced applications and innovative case studies for the next-generation optical networks based on modulation recognition using artificial neural networks, hardware ANN for gait generation of multi-legged robots, production of high-resolution soil property ANN maps, ANN and dynamic factor models to combine forecasts, ANN parameter recognition of engineering constants in Civil Engineering, ANN electricity consumption and generation forecasting, ANN for advanced process control, ANN breast cancer detection, ANN applications in biofuels, ANN modeling for manufacturing process optimization, spectral interference correction using a large-size spectrometer and ANN-based deep learning, solar radiation ANN prediction using NARX model, and ANN data assimilation for an atmospheric general circulation model.

Comprehensive and impeccably edited, Neural Networks in QSAR and Drug Design is the first book to present an all-inclusive coverage of the topic. The book provides a practice-oriented introduction to the different neural network paradigms, allowing the reader to easily understand and reproduce the results demonstrated. Numerous examples are detailed, demonstrating a variety of applications to QSAR and drug design.The contributors include some of the most distinguished names in the field, and the book provides an exhaustive bibliography, guiding readers to all the literature related to a particular type of application or neural network paradigm. The extensive index acts as a guide to the book, and makes retrieving information from chapters an easy task. A further research aid is a list of software with indications of availablility and price, as well as the editors scale rating the ease of use and interest/price ratio of each software package. The presentation of new, powerful tools for modeling molecular properties and the inclusion of many important neural network paradigms, coupled with extensive reference aids, makes Neural Networks in QSAR and Drug Design an essential reference source for those on the frontiers of this field. - Presents the first coverage of neural networks in QSAR and Drug Design - Allows easy understanding and reproduction of the results described within - Includes an exhaustive bibliography with more than 200 references - Provides a list of applicable software packages with availability and price

The quest for building systems that can function automatically has attracted a lot of attention over the centuries and created continuous research activities. As users of these systems we have never been satisfied, and demand more from the artifacts that are designed and manufactured. The current trend is to build autonomous systems that can adapt to changes in their environment. While there is a lot to be done before we reach this point, it is not possible to separate manufacturing systems from this trend. The desire to achieve fully automated manufacturing systems is here to stay. Manufacturing systems of the twenty-first century will demand more flexibility in product design, process planning, scheduling and process control. This may well be achieved through integrated software and hardware archi tectures that generate current decisions based on information collected from manufacturing systems environment, and execute these decisions by converting them into signals transferred through communication network. Manufacturing technology has not yet reached this state. However, the urge for achieving this goal is transferred into the term 'Intelligent Systems' that we started to use more in late 1980s. Knowledge-based systems, our first efforts in this endeavor, were not sufficient to generate the 'Intelligence' required - our quest still continues. Artificial neural network technology is becoming an integral part of intelligent manufacturing systems and will have a profound impact on the design of autonomous engineering systems over the next few years.

This book provides a thorough theoretical and practical introduction to the application of neural networks to pattern recognition and intelligent signal processing. It has been tested on students, unfamiliar with neural networks, who were able to pick up enough details to successfully complete their masters or final year undergraduate projects. The text also presents a comprehensive treatment of a class of neural networks called common bandwidth spherical basis function NNs, including the probabilistic NN, the modified probabilistic NN and the general regression NN.

Artificial neural networks may probably be the single most successful technology in the last two decades which has been widely used in a large variety of applications. The purpose of this book is to provide recent advances of artificial neural networks in industrial and control engineering applications. The book begins with a review of applications of artificial neural networks in textile industries. Particular applications in textile industries follow. Parts continue with applications in materials science and industry such as material identification, and estimation of material property and state, food industry such as meat, electric and power industry such as batteries and power systems, mechanical engineering such as engines and machines, and control and robotic engineering such as system control and identification, fault diagnosis systems, and robot manipulation. Thus, this book will be a fundamental source of recent advances and applications of artificial neural networks in industrial and control engineering areas. The target audience includes professors and students in engineering schools, and researchers and engineers in industries.

Computational Intelligence: Concepts to Implementations provides the most complete and practical coverage of computational intelligence tools and techniques to date. This book integrates various natural and engineering disciplines to establish Computational Intelligence. This is the first comprehensive textbook on the subject, supported with lots of practical examples. It asserts that computational intelligence rests on a foundation of evolutionary computation. This refreshing view has set the book apart from other books on computational intelligence. This book lays emphasis on practical applications and computational tools, which are very useful and important for further development of the computational intelligence field. Focusing on evolutionary computation, neural networks, and fuzzy logic, the authors have constructed an approach to thinking about and working with computational intelligence that has, in their extensive experience, proved highly effective. The book moves clearly and efficiently from concepts and paradigms to algorithms and implementation techniques by focusing, in the early chapters, on the specific con. It explores a number of key themes, including self-organization, complex adaptive systems, and emergent computation. It details the metrics and analytical tools needed to assess the performance of computational intelligence tools. The book concludes with a series of case studies that illustrate a wide range of successful applications. This book will appeal to professional and academic researchers in computational intelligence applications, tool development, and systems. - Moves clearly and efficiently from concepts and paradigms to algorithms and implementation techniques by focusing, in the early chapters, on the specific concepts and paradigms that inform the authors' methodologies - Explores a number of key themes, including self-organization, complex adaptive systems, and emergent computation - Details the metrics and analytical tools needed to assess the performance of computational intelligence tools - Concludes with a series of case studies that illustrate a wide range of successful applications - Presents code examples in C and C++ - Provides, at the end of each chapter, review questions and exercises suitable for graduate students, as well as researchers and practitioners engaged in self-study

This two-volume proceedings compilation is a selection of research papers presented at the ICANN-92. The scope of the volumes is interdisciplinary, ranging from the minutiae of VLSI hardware, to new discoveries in neurobiology, through to the workings of the human mind. USA and European research is well represented, including not only new thoughts from old masters but also a large number of first-time authors who are ensuring the continued development of the field.

The world is witnessing the rapid evolution of its own nervous system by an unparalleled growth in communication technology. Like the evolution of the nervous systems in animals, this growth is being driven by a survival-of-the-fittest-mechanism. In telecommunications, the entities that fuel this growth are companies and nations who compete with each other. Companies with superior information systems can outrun and outsmart others because they serve their customers better. On the threshold of an explosion in the variety, speed and usefulness of telecommunication networks, neural network researchers can make important contributions to this emerging new telecommunications infrastructure. The first International Workshop on Applications of Neural Networks to Telecommunications (IWANNT) was planned in response to the telecommunications industry's needs for new adaptive technologies. This workshop featured 50 talks and posters that were selected by an organizing committee of experts in both telecommunications and neural networks. These proceedings will also be available on-line in an electronic format providing multimedia figures, cross-referencing, and annotation.

Neural networks have received a great deal of attention among scientists and engineers. In chemical engineering, neural computing has moved from pioneering projects toward mainstream industrial applications. This book introduces the fundamental principles of neural computing, and is the first to focus on its practical applications in bioprocessing and chemical engineering. Examples, problems, and 10 detailed case studies demonstrate how to develop, train, and apply neural networks. A disk containing input data files for all illustrative examples, case studies, and practice problems provides the opportunity for hands-on experience. An important goal of the book is to help the student or practitioner learn and implement neural networks quickly and inexpensively using commercially available, PC-based software tools. Detailed network specifications and training procedures are included for all neural network examples discussed in the book.Each chapter contains an introduction, chapter summary, references to further reading, practice problems, and a section on nomenclatureIncludes a PC-compatible disk containing input data files for examples, case studies, and practice problemsPresents 10 detailed case studiesContains an extensive glossary, explaining terminology used in neural network applications in science and engineeringProvides examples, problems, and ten detailed case studies of neural computing applications, including:Process fault-diagnosis of a chemical reactorLeonardKramer fault-classification problemProcess fault-diagnosis for an unsteady-state continuous stirred-tank reactor systemClassification of protein secondary-structure categoriesQuantitative prediction and regression analysis of complex chemical kineticsSoftware-based sensors for quantitative predictions of product compositions from flourescent spectra in bioprocessingQuality control and optimization of an autoclave curing process for manufacturing composite materialsPredictive modeling of an experimental batch fermentation processSupervisory control of the Tennessee Eastman plantwide control problemPredictive modeling and optimal design of extractive bioseparation in aqueous two-phase systems