Discrete Neural Computation
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Discrete Neural Computation
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Author : Kai-Yeung Siu
language : en
Publisher: Prentice Hall
Release Date : 1995
Discrete Neural Computation written by Kai-Yeung Siu and has been published by Prentice Hall this book supported file pdf, txt, epub, kindle and other format this book has been release on 1995 with Computers categories.
Written by the three leading authorities in the field, this book brings together -- in one volume -- the recent developments in discrete neural computation, with a focus on neural networks with discrete inputs and outputs. It integrates a variety of important ideas and analytical techniques, and establishes a theoretical foundation for discrete neural computation. Discusses the basic models for discrete neural computation and the fundamental concepts in computational complexity; establishes efficient designs of threshold circuits for computing various functions; develops techniques for analyzing the computational power of neural models. A reference/text for computer scientists and researchers involved with neural computation and related disciplines.
Discrete Neural Computation Basics
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Author : Pasquale De Marco
language : en
Publisher: Pasquale De Marco
Release Date : 2025-04-26
Discrete Neural Computation Basics written by Pasquale De Marco and has been published by Pasquale De Marco this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-26 with Technology & Engineering categories.
Discrete neural computation is a rapidly growing field that has the potential to revolutionize many aspects of our lives. Discrete neural networks are a type of artificial neural network that uses discrete-valued inputs and outputs instead of continuous-valued inputs and outputs. This makes them particularly well-suited for applications in areas such as computer vision, natural language processing, and robotics. In this book, we provide a comprehensive introduction to the foundations of discrete neural computation. We cover everything from the basic principles of discrete neural network learning to the latest advances in discrete neural network optimization. We also explore a variety of applications of discrete neural networks, including image classification, object detection, speech recognition, and robot control. The book is divided into five chapters. The first chapter provides an introduction to discrete neural computation. We discuss the history of discrete neural computation, the different types of discrete neural networks, and the applications of discrete neural networks. The second chapter covers the basic principles of discrete neural network learning. We discuss the different types of learning algorithms that are used to train discrete neural networks, and we explore the challenges that are associated with training discrete neural networks. The third chapter covers the different types of optimization techniques that are used to improve the performance of discrete neural networks. We discuss the different types of optimization algorithms that are used to train discrete neural networks, and we explore the challenges that are associated with optimizing discrete neural networks. The fourth chapter covers a variety of applications of discrete neural networks. We discuss the use of discrete neural networks for image classification, object detection, speech recognition, and robot control. We also explore the challenges that are associated with using discrete neural networks for these applications. The fifth chapter provides a summary of the key points that were covered in the book. We also discuss the future directions of research in discrete neural computation. This book is intended for readers with a basic understanding of artificial neural networks. It is also intended for readers who are interested in learning more about the foundations of discrete neural computation. We hope that this book will provide readers with a comprehensive understanding of the foundations of discrete neural computation. We also hope that this book will inspire readers to explore the many potential applications of discrete neural networks. If you like this book, write a review on google books!
Handbook Of Neural Computation
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Author : E Fiesler
language : en
Publisher: CRC Press
Release Date : 2020-01-15
Handbook Of Neural Computation written by E Fiesler and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-01-15 with Computers categories.
The Handbook of Neural Computation is a practical, hands-on guide to the design and implementation of neural networks used by scientists and engineers to tackle difficult and/or time-consuming problems. The handbook bridges an information pathway between scientists and engineers in different disciplines who apply neural networks to similar probl
Discrete Time High Order Neural Control
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Author : Edgar N. Sanchez
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-04-29
Discrete Time High Order Neural Control written by Edgar N. Sanchez 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 2008-04-29 with Mathematics categories.
Neural networks have become a well-established methodology as exempli?ed by their applications to identi?cation and control of general nonlinear and complex systems; the use of high order neural networks for modeling and learning has recently increased. Usingneuralnetworks,controlalgorithmscanbedevelopedtoberobustto uncertainties and modeling errors. The most used NN structures are Feedf- ward networks and Recurrent networks. The latter type o?ers a better suited tool to model and control of nonlinear systems. There exist di?erent training algorithms for neural networks, which, h- ever, normally encounter some technical problems such as local minima, slow learning, and high sensitivity to initial conditions, among others. As a viable alternative, new training algorithms, for example, those based on Kalman ?ltering, have been proposed. There already exists publications about trajectory tracking using neural networks; however, most of those works were developed for continuous-time systems. On the other hand, while extensive literature is available for linear discrete-timecontrolsystem,nonlineardiscrete-timecontroldesigntechniques have not been discussed to the same degree. Besides, discrete-time neural networks are better ?tted for real-time implementations.
Limitations And Future Trends In Neural Computation
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Author :
language : en
Publisher: IOS Press
Release Date : 2003
Limitations And Future Trends In Neural Computation written by and has been published by IOS Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003 with Neural computers categories.
Discrete Mathematics Of Neural Networks
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Author : Martin Anthony
language : en
Publisher: SIAM
Release Date : 2001-01-01
Discrete Mathematics Of Neural Networks written by Martin Anthony and has been published by SIAM this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001-01-01 with Computers categories.
This concise, readable book provides a sampling of the very large, active, and expanding field of artificial neural network theory. It considers select areas of discrete mathematics linking combinatorics and the theory of the simplest types of artificial neural networks. Neural networks have emerged as a key technology in many fields of application, and an understanding of the theories concerning what such systems can and cannot do is essential. Some classical results are presented with accessible proofs, together with some more recent perspectives, such as those obtained by considering decision lists. In addition, probabilistic models of neural network learning are discussed. Graph theory, some partially ordered set theory, computational complexity, and discrete probability are among the mathematical topics involved. Pointers to further reading and an extensive bibliography make this book a good starting point for research in discrete mathematics and neural networks.
Emergent Neural Computational Architectures Based On Neuroscience
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Author : Stefan Wermter
language : en
Publisher: Springer
Release Date : 2003-05-15
Emergent Neural Computational Architectures Based On Neuroscience written by Stefan Wermter and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-05-15 with Computers categories.
It is generally understood that the present approachs to computing do not have the performance, flexibility, and reliability of biological information processing systems. Although there is a comprehensive body of knowledge regarding how information processing occurs in the brain and central nervous system this has had little impact on mainstream computing so far. This book presents a broad spectrum of current research into biologically inspired computational systems and thus contributes towards developing new computational approaches based on neuroscience. The 39 revised full papers by leading researchers were carefully selected and reviewed for inclusion in this anthology. Besides an introductory overview by the volume editors, the book offers topical parts on modular organization and robustness, timing and synchronization, and learning and memory storage.
Neural Computation
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Author :
language : en
Publisher:
Release Date : 2005
Neural Computation written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with Neural computers categories.
Sofsem 99 Theory And Practice Of Informatics
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Author : Jan Pavelka
language : en
Publisher: Springer
Release Date : 2003-07-31
Sofsem 99 Theory And Practice Of Informatics written by Jan Pavelka and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-07-31 with Computers categories.
This year the SOFSEM conference is coming back to Milovy in Moravia to th be held for the 26 time. Although born as a local Czechoslovak event 25 years ago SOFSEM did not miss the opportunity oe red in 1989 by the newly found freedom in our part of Europe and has evolved into a full-?edged international conference. For all the changes, however, it has kept its generalist and mul- disciplinarycharacter.Thetracksofinvitedtalks,rangingfromTrendsinTheory to Software and Information Engineering, attest to this. Apart from the topics mentioned above, SOFSEM’99 oer s invited talks exploring core technologies, talks tracing the path from data to knowledge, and those describing a wide variety of applications. TherichcollectionofinvitedtalkspresentsonetraditionalfacetofSOFSEM: that of a winter school, in which IT researchers and professionals get an opp- tunity to see more of the large pasture of today’s computing than just their favourite grazing corner. To facilitate this purpose the prominent researchers delivering invited talks usually start with a broad overview of the state of the art in a wider area and then gradually focus on their particular subject.
Handbook Of Neural Computing Applications
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Author : Alianna J. Maren
language : en
Publisher: Academic Press
Release Date : 2014-05-10
Handbook Of Neural Computing Applications written by Alianna J. Maren and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-05-10 with Computers categories.
Handbook of Neural Computing Applications is a collection of articles that deals with neural networks. Some papers review the biology of neural networks, their type and function (structure, dynamics, and learning) and compare a back-propagating perceptron with a Boltzmann machine, or a Hopfield network with a Brain-State-in-a-Box network. Other papers deal with specific neural network types, and also on selecting, configuring, and implementing neural networks. Other papers address specific applications including neurocontrol for the benefit of control engineers and for neural networks researchers. Other applications involve signal processing, spatio-temporal pattern recognition, medical diagnoses, fault diagnoses, robotics, business, data communications, data compression, and adaptive man-machine systems. One paper describes data compression and dimensionality reduction methods that have characteristics, such as high compression ratios to facilitate data storage, strong discrimination of novel data from baseline, rapid operation for software and hardware, as well as the ability to recognized loss of data during compression or reconstruction. The collection can prove helpful for programmers, computer engineers, computer technicians, and computer instructors dealing with many aspects of computers related to programming, hardware interface, networking, engineering or design.