Neural Networks For Optimization And Signal Processing
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Neural Networks For Optimization And Signal Processing
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Author : Andrzej Cichocki
language : en
Publisher:
Release Date : 1993-01
Neural Networks For Optimization And Signal Processing written by Andrzej Cichocki and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1993-01 with Mathematical optimization categories.
Neural Networks For Optimization And Signal Processing
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Author : Andrzej Cichocki
language : en
Publisher: John Wiley & Sons
Release Date : 1993-06-07
Neural Networks For Optimization And Signal Processing written by Andrzej Cichocki and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 1993-06-07 with Computers categories.
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.
Process Neural Networks
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Author : Xingui He
language : en
Publisher: Springer Science & Business Media
Release Date : 2010-07-05
Process Neural Networks written by Xingui He and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-07-05 with Computers categories.
"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. Xingui He is a member of Chinese Academy of Engineering and also a professor at the School of Electronic Engineering and Computer Science, Peking University, China, where Shaohua Xu also serves as a professor.
Applied Neural Networks For Signal Processing
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Author : Fa-Long Luo
language : en
Publisher: Cambridge University Press
Release Date : 1998
Applied Neural Networks For Signal Processing written by Fa-Long Luo and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1998 with Computers categories.
A comprehensive introduction to the use of neural networks in signal processing.
Neural Information Processing And Vlsi
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Author : Bing J. Sheu
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Neural Information Processing And Vlsi written by Bing J. Sheu and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Technology & Engineering categories.
Neural Information Processing and VLSI provides a unified treatment of this important subject for use in classrooms, industry, and research laboratories, in order to develop advanced artificial and biologically-inspired neural networks using compact analog and digital VLSI parallel processing techniques. Neural Information Processing and VLSI systematically presents various neural network paradigms, computing architectures, and the associated electronic/optical implementations using efficient VLSI design methodologies. Conventional digital machines cannot perform computationally-intensive tasks with satisfactory performance in such areas as intelligent perception, including visual and auditory signal processing, recognition, understanding, and logical reasoning (where the human being and even a small living animal can do a superb job). Recent research advances in artificial and biological neural networks have established an important foundation for high-performance information processing with more efficient use of computing resources. The secret lies in the design optimization at various levels of computing and communication of intelligent machines. Each neural network system consists of massively paralleled and distributed signal processors with every processor performing very simple operations, thus consuming little power. Large computational capabilities of these systems in the range of some hundred giga to several tera operations per second are derived from collectively parallel processing and efficient data routing, through well-structured interconnection networks. Deep-submicron very large-scale integration (VLSI) technologies can integrate tens of millions of transistors in a single silicon chip for complex signal processing and information manipulation. The book is suitable for those interested in efficient neurocomputing as well as those curious about neural network system applications. It has beenespecially prepared for use as a text for advanced undergraduate and first year graduate students, and is an excellent reference book for researchers and scientists working in the fields covered.
Fuzzy Systems And Soft Computing In Nuclear Engineering
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Author : Da Ruan
language : en
Publisher: Springer Science & Business Media
Release Date : 2000-01-14
Fuzzy Systems And Soft Computing In Nuclear Engineering written by Da Ruan and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2000-01-14 with Business & Economics categories.
This book is an organized edited collection of twenty-one contributed chapters covering nuclear engineering applications of fuzzy systems, neural networks, genetic algorithms and other soft computing techniques. All chapters are either updated review or original contributions by leading researchers written exclusively for this volume. The volume highlights the advantages of applying fuzzy systems and soft computing in nuclear engineering, which can be viewed as complementary to traditional methods. As a result, fuzzy sets and soft computing provide a powerful tool for solving intricate problems pertaining in nuclear engineering. Each chapter of the book is self-contained and also indicates the future research direction on this topic of applications of fuzzy systems and soft computing in nuclear engineering.
Neural Networks In Bioprocessing And Chemical Engineering
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Author : D. R. Baughman
language : en
Publisher: Academic Press
Release Date : 1995
Neural Networks In Bioprocessing And Chemical Engineering written by D. R. Baughman and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1995 with Computers categories.
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.
Speech Audio Image And Biomedical Signal Processing Using Neural Networks
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Author : Bhanu Prasad
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-01-03
Speech Audio Image And Biomedical Signal Processing Using Neural Networks written by Bhanu Prasad and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-01-03 with Computers categories.
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.
Sensitivity Analysis For Neural Networks
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Author : Daniel S. Yeung
language : en
Publisher: Springer Science & Business Media
Release Date : 2009-11-09
Sensitivity Analysis For Neural Networks written by Daniel S. Yeung and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-11-09 with Computers categories.
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. This is the first book to present a systematic description of sensitivity analysis methods for artificial neural networks. It covers sensitivity analysis of multilayer perceptron neural networks and radial basis function neural networks, two widely used models in the machine learning field. The authors examine the applications of such analysis in tasks such as feature selection, sample reduction, and network optimization. The book will be useful for engineers applying neural network sensitivity analysis to solve practical problems, and for researchers interested in foundational problems in neural networks.
Neural Computing For Optimization And Combinatorics
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Author : Jun Li Jim Wang
language : en
Publisher: World Scientific
Release Date : 1996
Neural Computing For Optimization And Combinatorics written by Jun Li Jim Wang and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 1996 with Mathematics categories.
Since Hopfield proposed neural network computing for optimization and combinatorics problems, many neural network investigators have been working on optimization problems. In this book a variety of optimization problems and combinatorics problems are presented by respective experts.A very useful reference book for those who want to solve real-world applications, this book contains applications in graph theory, mathematics, stochastic computing including the multiple relaxation, associative memory and control, resource allocation problems, system identification and dynamic control, and job-stop scheduling.