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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:
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.
Computational intelligence is an emerging field in computer science which combines fuzzy logic, neural networks, and genetic algorithms for a flexible yet powerful approach to scientific computing. Because computational intelligence combines three interrelated, mathematically-based tools, it has a wide variety of applications, from engineering and process control to experts systems. This book takes a hands-on, desktop-applications approach to the topic, featuring examples of specific real-world implementations and detailed case studies, with all pertinent code and software included on a floppy disk packaged with the book. * * Concise introduction to the concepts of fuzzy logic, neural networks, and genetic algorithms, and how they relate to one another within the context of computational intelligence. * Computational intellignece applications, including self-organizing feature maps, fuzzy calculator, evolutionary programming, and fuzzy neural networks. * Detailed case studies from engineering (F-16 flight system), systems control (mass transit scheduling), and medicine (appendicitis diagnosis). * Windows floppy disk with both source code and executable, self-contained programs for desktop implementation of all of the book's applications.
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 book provides a comprehensive view of pattern recognition concepts and methods, illustrated with real-life applications in several areas. A CD-ROM offered with the book includes datasets and software tools, making it easier to follow in a hands-on fashion, right from the start.
Use Java to develop neural network applications in this practical book. After learning the rules involved in neural network processing, you will manually process the first neural network example. This covers the internals of front and back propagation, and facilitates the understanding of the main principles of neural network processing. Artificial Neural Networks with Java also teaches you how to prepare the data to be used in neural network development and suggests various techniques of data preparation for many unconventional tasks. The next big topic discussed in the book is using Java for neural network processing. You will use the Encog Java framework and discover how to do rapid development with Encog, allowing you to create large-scale neural network applications. The book also discusses the inability of neural networks to approximate complex non-continuous functions, and it introduces the micro-batch method that solves this issue. The step-by-step approach includes plenty of examples, diagrams, and screen shots to help you grasp the concepts quickly and easily. What You Will LearnPrepare your data for many different tasks Carry out some unusual neural network tasks Create neural network to process non-continuous functions Select and improve the development model Who This Book Is For Intermediate machine learning and deep learning developers who are interested in switching to Java.
In today's business environment, reliability and maintenance drastically affect the three key elements of competitiveness - quality, cost, and product lead time. Well-maintained machines hold tolerances better, help reduce scrap and rework, and raise consistency and quality of the part in addition to cutting total production costs. Today, many factories are still performing maintenance on equipment in a reactive manner due to a lack of understanding about machine performance behaviour. To improve production efficiency, computer-aided maintenance and diagnostic methodology must be applied effectively in manufacturing. This book focuses on the fundamental principles of predictive maintenance and diagnostic engineering. In addition to covering the relevant theory, techniques and methodologies in maintenance engineering, the book also provides numerous case studies and examples illustrating the successful application of the principles and techniques outlined.
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.
