Download and read online Applied Neural Networks for Signal Processing in PDF and EPUB A comprehensive introduction to the use of neural networks in signal processing.
Download and read online Handbook of Neural Network Signal Processing in PDF and EPUB The use of neural networks is permeating every area of signal processing. They can provide powerful means for solving many problems, especially in nonlinear, real-time, adaptive, and blind signal processing. The Handbook of Neural Network Signal Processing brings together applications that were previously scattered among various publications to provide an up-to-date, detailed treatment of the subject from an engineering point of view. The authors cover basic principles, modeling, algorithms, architectures, implementation procedures, and well-designed simulation examples of audio, video, speech, communication, geophysical, sonar, radar, medical, and many other signals. The subject of neural networks and their application to signal processing is constantly improving. You need a handy reference that will inform you of current applications in this new area. The Handbook of Neural Network Signal Processing provides this much needed service for all engineers and scientists in the field.
Download and read online Applying Neural Networks in PDF and EPUB In this computer-based era, neural networks are an invaluable tool. They have been applied extensively in business forecasting, machine health monitoring, process control, and laboratory data analysis due to their modeling capabilities. There are numerous applications for neural networks, but a great deal of care and expertise is necessary to keep a neural-based project in working order. This all-inclusive coverage gives you everything you need to put neural networks into practice. This informative book shows the reader how to plan, run, and benefit from a neural-based project without running into the roadblocks that often crop up. Theauthor uses the most popular type of neural network, the Multi-Layer Perceptron, and presents every step of its development. Each chapter presents a subsequent stage in network development through easy-to-follow discussion. Every decision and possible problem is considered in depth, and solutions are offered. The book includes a how-to-do-it reference section, and a set of worked examples. The second half of the book examines the sucessful application of neural networks in fields including signal processing, financial prediction, business decision support, and process monitoring and control. The book comes complete with a disk containing C and C++ programs to get you started. Key Features *Divides chapters into three sections for quick reference: Discussion, How to do it, and Examples * Examines many case studies and real world examples to illustrate the methods presented * Includes a disk with C and C++ programs which implement many of the techniques discussed in the text * Allows the reader to devolop a neural network based solution
Download and read online Speech Audio Image and Biomedical Signal Processing using Neural Networks in PDF and EPUB Humans are remarkable in processing speech, audio, image and some biomedical signals. Artificial neural networks are proved to be successful in performing several cognitive, industrial and scientific tasks. This peer reviewed book presents some recent advances and surveys on the applications of artificial neural networks in the areas of speech, audio, image and biomedical signal processing. It chapters are prepared by some reputed researchers and practitioners around the globe.
Download and read online Artificial Neural Networks ICANN 2009 in PDF and EPUB This volume is part of the two-volume proceedings of the 19th International Conf- ence on Artificial Neural Networks (ICANN 2009), which was held in Cyprus during September 14–17, 2009. The ICANN conference is an annual meeting sp- sored by the European Neural Network Society (ENNS), in cooperation with the - ternational Neural Network Society (INNS) and the Japanese Neural Network Society (JNNS). ICANN 2009 was technically sponsored by the IEEE Computational Intel- gence Society. This series of conferences has been held annually since 1991 in various European countries and covers the field of neurocomputing, learning systems and related areas. Artificial neural networks provide an information-processing structure inspired by biological nervous systems. They consist of a large number of highly interconnected processing elements, with the capability of learning by example. The field of artificial neural networks has evolved significantly in the last two decades, with active partici- tion from diverse fields, such as engineering, computer science, mathematics, artificial intelligence, system theory, biology, operations research, and neuroscience. Artificial neural networks have been widely applied for pattern recognition, control, optimization, image processing, classification, signal processing, etc.
Download and read online Neural networks for optimization and signal processing in PDF and EPUB A topical introduction on the ability of artificial neural networks to not only solve on-line a wide range of optimization problems but also to create new techniques and architectures. Provides in-depth coverage of mathematical modeling along with illustrative computer simulation results.
Download and read online Advances in Computational Intelligence in PDF and EPUB This two-volume set LNCS 10305 and LNCS 10306 constitutes the refereed proceedings of the 14th International Work-Conference on Artificial Neural Networks, IWANN 2017, held in Cadiz, Spain, in June 2017. The 126 revised full papers presented in this double volume were carefully reviewed and selected from 199 submissions. The papers are organized in topical sections on Bio-inspired Computing; E-Health and Computational Biology; Human Computer Interaction; Image and Signal Processing; Mathematics for Neural Networks; Self-organizing Networks; Spiking Neurons; Artificial Neural Networks in Industry ANNI'17; Computational Intelligence Tools and Techniques for Biomedical Applications; Assistive Rehabilitation Technology; Computational Intelligence Methods for Time Series; Machine Learning Applied to Vision and Robotics; Human Activity Recognition for Health and Well-Being Applications; Software Testing and Intelligent Systems; Real World Applications of BCI Systems; Machine Learning in Imbalanced Domains; Surveillance and Rescue Systems and Algorithms for Unmanned Aerial Vehicles; End-User Development for Social Robotics; Artificial Intelligence and Games; and Supervised, Non-Supervised, Reinforcement and Statistical Algorithms.
Download and read online Artificial Neural Networks in Biomedicine in PDF and EPUB This volume provides a state-of-the-art survey of artificial neural network applications in biomedical diagnosis, laboratory data analysis and related practical areas. It looks at biomedical applications which involve customising neural network technology to resolve specific difficulties with data processing, and deals with applications relating to particular aspects of clinical practice and laboratory or medically-related analysis. Each chapter is self-contained with regard to the technology used, covering important technical points and implementation issues like the design of user interfaces and hardware/software platforms. Artificial Neural Networks in Biomedicine will be of interest to computer scientists and neural network practitioners who want to extend their knowledge of issues relevant to biomedical applications, developers of clinical computer systems, and medical researchers looking for new methods and computational tools.
Download and read online Neural Networks in a Softcomputing Framework in PDF and EPUB This concise but comprehensive textbook reviews the most popular neural-network methods and their associated techniques. Each chapter provides state-of-the-art descriptions of important major research results of the respective neural-network methods. A range of relevant computational intelligence topics, such as fuzzy logic and evolutionary algorithms – powerful tools for neural-network learning – are introduced. The systematic survey of neural-network models and exhaustive references list will point readers toward topics for future research. The algorithms outlined also make this textbook a valuable reference for scientists and practitioners working in pattern recognition, signal processing, speech and image processing, data analysis and artificial intelligence.
Download and read online Kalman Filtering and Neural Networks in PDF and EPUB State-of-the-art coverage of Kalman filter methods for the design of neural networks This self-contained book consists of seven chapters by expert contributors that discuss Kalman filtering as applied to the training and use of neural networks. Although the traditional approach to the subject is almost always linear, this book recognizes and deals with the fact that real problems are most often nonlinear. The first chapter offers an introductory treatment of Kalman filters with an emphasis on basic Kalman filter theory, Rauch-Tung-Striebel smoother, and the extended Kalman filter. Other chapters cover: An algorithm for the training of feedforward and recurrent multilayered perceptrons, based on the decoupled extended Kalman filter (DEKF) Applications of the DEKF learning algorithm to the study of image sequences and the dynamic reconstruction of chaotic processes The dual estimation problem Stochastic nonlinear dynamics: the expectation-maximization (EM) algorithm and the extended Kalman smoothing (EKS) algorithm The unscented Kalman filter Each chapter, with the exception of the introduction, includes illustrative applications of the learning algorithms described here, some of which involve the use of simulated and real-life data. Kalman Filtering and Neural Networks serves as an expert resource for researchers in neural networks and nonlinear dynamical systems. An Instructor's Manual presenting detailed solutions to all the problems in the book is available upon request from the Wiley Makerting Department.
Download and read online Neural Networks for Signal Processing in PDF and EPUB
Download and read online Digital Signal Processing in Power System Protection and Control in PDF and EPUB Digital Signal Processing in Power System Protection and Control bridges the gap between the theory of protection and control and the practical applications of protection equipment. Understanding how protection functions is crucial not only for equipment developers and manufacturers, but also for their users who need to install, set and operate the protection devices in an appropriate manner. After introductory chapters related to protection technology and functions, Digital Signal Processing in Power System Protection and Control presents the digital algorithms for signal filtering, followed by measurement algorithms of the most commonly-used protection criteria values and decision-making methods in protective relays. A large part of the book is devoted to the basic theory and applications of artificial intelligence techniques for protection and control. Fuzzy logic based schemes, artificial neural networks, expert systems and genetic algorithms with their advantages and drawbacks are discussed. AI techniques are compared and it is also shown how they can be combined to eliminate the disadvantages and magnify the useful features of particular techniques. The information provided in Digital Signal Processing in Power System Protection and Control can be useful for protection engineers working in utilities at various levels of the electricity network, as well as for students of electrical engineering, especially electrical power engineering. It may also be helpful for other readers who want to get acquainted with and to apply the filtering, measuring and decision-making algorithms for purposes other than protection and control, everywhere fast and on-line signal analysis is needed for proper functioning of the apparatus.
Download and read online Elements of Artificial Neural Networks in PDF and EPUB Elements of Artificial Neural Networks provides a clearly organized general introduction, focusing on a broad range of algorithms, for students and others who want to use neural networks rather than simply study them. The authors, who have been developing and team teaching the material in a one-semester course over the past six years, describe most of the basic neural network models (with several detailed solved examples) and discuss the rationale and advantages of the models, as well as their limitations. The approach is practical and open-minded and requires very little mathematical or technical background. Written from a computer science and statistics point of view, the text stresses links to contiguous fields and can easily serve as a first course for students in economics and management. The opening chapter sets the stage, presenting the basic concepts in a clear and objective way and tackling important -- yet rarely addressed -- questions related to the use of neural networks in practical situations. Subsequent chapters on supervised learning (single layer and multilayer networks), unsupervised learning, and associative models are structured around classes of problems to which networks can be applied. Applications are discussed along with the algorithms. A separate chapter takes up optimization methods. The most frequently used algorithms, such as backpropagation, are introduced early on, right after perceptrons, so that these can form the basis for initiating course projects. Algorithms published as late as 1995 are also included. All of the algorithms are presented using block-structured pseudo-code, and exercises are provided throughout. Software implementing many commonly used neural network algorithms is available at the book's website. Transparency masters, including abbreviated text and figures for the entire book, are available for instructors using the text.
Download and read online Neural Networks for Signal Processing in PDF and EPUB
Download and read online An Introduction to Neural Networks in PDF and EPUB Though mathematical ideas underpin the study of neural networks, the author presents the fundamentals without the full mathematical apparatus. All aspects of the field are tackled, including artificial neurons as models of their real counterparts; the geometry of network action in pattern space; gradient descent methods, including back-propagation; associative memory and Hopfield nets; and self-organization and feature maps. The traditionally difficult topic of adaptive resonance theory is clarified within a hierarchical description of its operation. The book also includes several real-world examples to provide a concrete focus. This should enhance its appeal to those involved in the design, construction and management of networks in commercial environments and who wish to improve their understanding of network simulator packages. As a comprehensive and highly accessible introduction to one of the most important topics in cognitive and computer science, this volume should interest a wide range of readers, both students and professionals, in cognitive science, psychology, computer science and electrical engineering.