Neural Networks And Analog Computation

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Neural Networks and Analog Computation

Author : Hava T. Siegelmann
Publisher : Springer Science & Business Media
ISBN 13 : 146120707X
Total Pages : 193 pages
Book Rating : 4.4/5 (612 download)

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Book Synopsis Neural Networks and Analog Computation by : Hava T. Siegelmann

Download or read book Neural Networks and Analog Computation written by Hava T. Siegelmann and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 193 pages. Available in PDF, EPUB and Kindle. Book excerpt: The theoretical foundations of Neural Networks and Analog Computation conceptualize neural networks as a particular type of computer consisting of multiple assemblies of basic processors interconnected in an intricate structure. Examining these networks under various resource constraints reveals a continuum of computational devices, several of which coincide with well-known classical models. On a mathematical level, the treatment of neural computations enriches the theory of computation but also explicated the computational complexity associated with biological networks, adaptive engineering tools, and related models from the fields of control theory and nonlinear dynamics. The material in this book will be of interest to researchers in a variety of engineering and applied sciences disciplines. In addition, the work may provide the base of a graduate-level seminar in neural networks for computer science students.

Neural Networks and Analog Computation

Author : Hava Siegelmann
Publisher : Springer Science & Business Media
ISBN 13 : 9780817639495
Total Pages : 208 pages
Book Rating : 4.6/5 (394 download)

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Book Synopsis Neural Networks and Analog Computation by : Hava Siegelmann

Download or read book Neural Networks and Analog Computation written by Hava Siegelmann and published by Springer Science & Business Media. This book was released on 1998-12-01 with total page 208 pages. Available in PDF, EPUB and Kindle. Book excerpt: The theoretical foundations of Neural Networks and Analog Computation conceptualize neural networks as a particular type of computer consisting of multiple assemblies of basic processors interconnected in an intricate structure. Examining these networks under various resource constraints reveals a continuum of computational devices, several of which coincide with well-known classical models. On a mathematical level, the treatment of neural computations enriches the theory of computation but also explicated the computational complexity associated with biological networks, adaptive engineering tools, and related models from the fields of control theory and nonlinear dynamics. The material in this book will be of interest to researchers in a variety of engineering and applied sciences disciplines. In addition, the work may provide the base of a graduate-level seminar in neural networks for computer science students.

Analog VLSI and Neural Systems

Author : Carver Mead
Publisher : Addison Wesley Publishing Company
ISBN 13 :
Total Pages : 416 pages
Book Rating : 4.4/5 (91 download)

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Book Synopsis Analog VLSI and Neural Systems by : Carver Mead

Download or read book Analog VLSI and Neural Systems written by Carver Mead and published by Addison Wesley Publishing Company. This book was released on 1989 with total page 416 pages. Available in PDF, EPUB and Kindle. Book excerpt: A self-contained text, suitable for a broad audience. Presents basic concepts in electronics, transistor physics, and neurobiology for readers without backgrounds in those areas. Annotation copyrighted by Book News, Inc., Portland, OR

Neural Networks: Computational Models and Applications

Author : Huajin Tang
Publisher : Springer Science & Business Media
ISBN 13 : 3540692258
Total Pages : 310 pages
Book Rating : 4.5/5 (46 download)

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Book Synopsis Neural Networks: Computational Models and Applications by : Huajin Tang

Download or read book Neural Networks: Computational Models and Applications written by Huajin Tang and published by Springer Science & Business Media. This book was released on 2007-03-12 with total page 310 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neural Networks: Computational Models and Applications presents important theoretical and practical issues in neural networks, including the learning algorithms of feed-forward neural networks, various dynamical properties of recurrent neural networks, winner-take-all networks and their applications in broad manifolds of computational intelligence: pattern recognition, uniform approximation, constrained optimization, NP-hard problems, and image segmentation. The book offers a compact, insightful understanding of the broad and rapidly growing neural networks domain.

Encyclopedia of Complexity and Systems Science

Author :
Publisher : Springer
ISBN 13 : 9780387758886
Total Pages : 10398 pages
Book Rating : 4.7/5 (588 download)

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Book Synopsis Encyclopedia of Complexity and Systems Science by :

Download or read book Encyclopedia of Complexity and Systems Science written by and published by Springer. This book was released on 2009-06-26 with total page 10398 pages. Available in PDF, EPUB and Kindle. Book excerpt: This encyclopedia provides an authoritative single source for understanding and applying the concepts of complexity theory together with the tools and measures for analyzing complex systems in all fields of science and engineering. It links fundamental concepts of mathematics and computational sciences to applications in the physical sciences, engineering, biomedicine, economics and the social sciences.

Efficient Processing of Deep Neural Networks

Author : Vivienne Sze
Publisher : Springer Nature
ISBN 13 : 3031017668
Total Pages : 254 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Efficient Processing of Deep Neural Networks by : Vivienne Sze

Download or read book Efficient Processing of Deep Neural Networks written by Vivienne Sze and published by Springer Nature. This book was released on 2022-05-31 with total page 254 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a structured treatment of the key principles and techniques for enabling efficient processing of deep neural networks (DNNs). DNNs are currently widely used for many artificial intelligence (AI) applications, including computer vision, speech recognition, and robotics. While DNNs deliver state-of-the-art accuracy on many AI tasks, it comes at the cost of high computational complexity. Therefore, techniques that enable efficient processing of deep neural networks to improve key metrics—such as energy-efficiency, throughput, and latency—without sacrificing accuracy or increasing hardware costs are critical to enabling the wide deployment of DNNs in AI systems. The book includes background on DNN processing; a description and taxonomy of hardware architectural approaches for designing DNN accelerators; key metrics for evaluating and comparing different designs; features of DNN processing that are amenable to hardware/algorithm co-design to improve energy efficiency and throughput; and opportunities for applying new technologies. Readers will find a structured introduction to the field as well as formalization and organization of key concepts from contemporary work that provide insights that may spark new ideas.

Computable Analysis

Author : Klaus Weihrauch
Publisher : Springer Science & Business Media
ISBN 13 : 9783540668176
Total Pages : 312 pages
Book Rating : 4.6/5 (681 download)

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Book Synopsis Computable Analysis by : Klaus Weihrauch

Download or read book Computable Analysis written by Klaus Weihrauch and published by Springer Science & Business Media. This book was released on 2000-09-14 with total page 312 pages. Available in PDF, EPUB and Kindle. Book excerpt: Merging fundamental concepts of analysis and recursion theory to a new exciting theory, this book provides a solid fundament for studying various aspects of computability and complexity in analysis. It is the result of an introductory course given for several years and is written in a style suitable for graduate-level and senior students in computer science and mathematics. Many examples illustrate the new concepts while numerous exercises of varying difficulty extend the material and stimulate readers to work actively on the text.

Analog VLSI Implementation of Neural Systems

Author : Carver Mead
Publisher : Springer Science & Business Media
ISBN 13 : 1461316391
Total Pages : 250 pages
Book Rating : 4.4/5 (613 download)

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Book Synopsis Analog VLSI Implementation of Neural Systems by : Carver Mead

Download or read book Analog VLSI Implementation of Neural Systems written by Carver Mead and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 250 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume contains the proceedings of a workshop on Analog Integrated Neural Systems held May 8, 1989, in connection with the International Symposium on Circuits and Systems. The presentations were chosen to encompass the entire range of topics currently under study in this exciting new discipline. Stringent acceptance requirements were placed on contributions: (1) each description was required to include detailed characterization of a working chip, and (2) each design was not to have been published previously. In several cases, the status of the project was not known until a few weeks before the meeting date. As a result, some of the most recent innovative work in the field was presented. Because this discipline is evolving rapidly, each project is very much a work in progress. Authors were asked to devote considerable attention to the shortcomings of their designs, as well as to the notable successes they achieved. In this way, other workers can now avoid stumbling into the same traps, and evolution can proceed more rapidly (and less painfully). The chapters in this volume are presented in the same order as the corresponding presentations at the workshop. The first two chapters are concerned with fmding solutions to complex optimization problems under a predefmed set of constraints. The first chapter reports what is, to the best of our knowledge, the first neural-chip design. In each case, the physics of the underlying electronic medium is used to represent a cost function in a natural way, using only nearest-neighbor connectivity.

Analogue Neural VLSI

Author : Alan F. Murray
Publisher :
ISBN 13 :
Total Pages : 176 pages
Book Rating : 4.3/5 (91 download)

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Book Synopsis Analogue Neural VLSI by : Alan F. Murray

Download or read book Analogue Neural VLSI written by Alan F. Murray and published by . This book was released on 1994 with total page 176 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Analog and Hybrid Computer Programming

Author : Bernd Ulmann
Publisher : Walter de Gruyter GmbH & Co KG
ISBN 13 : 3110787881
Total Pages : 441 pages
Book Rating : 4.1/5 (17 download)

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Book Synopsis Analog and Hybrid Computer Programming by : Bernd Ulmann

Download or read book Analog and Hybrid Computer Programming written by Bernd Ulmann and published by Walter de Gruyter GmbH & Co KG. This book was released on 2023-05-22 with total page 441 pages. Available in PDF, EPUB and Kindle. Book excerpt: As classic digital computers are about to reach their physical and architectural boundaries, interest in unconventional approaches to computing, such as quantum and analog computers, is rapidly increasing. For a wide variety of practical applications, analog computers can outperform classic digital computers in terms of both raw computational speed and energy efficiency. This makes them ideally suited a co-processors to digital computers, thus forming hybrid computers. This second edition of "Analog and Hybrid Computer Programming" provides a thorough introduction to the programming of analog and hybrid computers. It contains a wealth of practical examples, ranging from simple problems such as radioactive decay, harmonic oscillators, and chemical reaction kinetics to advanced topics which include the simulation of neurons, chaotic systems such as a double-pendulum simulation and many more. In addition to these examples, it contains a chapter on special functions which can be used as "subroutines" in an analog computer setup.

Mathematical Perspectives on Neural Networks

Author : Paul Smolensky
Publisher : Psychology Press
ISBN 13 : 1134773013
Total Pages : 890 pages
Book Rating : 4.1/5 (347 download)

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Book Synopsis Mathematical Perspectives on Neural Networks by : Paul Smolensky

Download or read book Mathematical Perspectives on Neural Networks written by Paul Smolensky and published by Psychology Press. This book was released on 2013-05-13 with total page 890 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recent years have seen an explosion of new mathematical results on learning and processing in neural networks. This body of results rests on a breadth of mathematical background which even few specialists possess. In a format intermediate between a textbook and a collection of research articles, this book has been assembled to present a sample of these results, and to fill in the necessary background, in such areas as computability theory, computational complexity theory, the theory of analog computation, stochastic processes, dynamical systems, control theory, time-series analysis, Bayesian analysis, regularization theory, information theory, computational learning theory, and mathematical statistics. Mathematical models of neural networks display an amazing richness and diversity. Neural networks can be formally modeled as computational systems, as physical or dynamical systems, and as statistical analyzers. Within each of these three broad perspectives, there are a number of particular approaches. For each of 16 particular mathematical perspectives on neural networks, the contributing authors provide introductions to the background mathematics, and address questions such as: * Exactly what mathematical systems are used to model neural networks from the given perspective? * What formal questions about neural networks can then be addressed? * What are typical results that can be obtained? and * What are the outstanding open problems? A distinctive feature of this volume is that for each perspective presented in one of the contributed chapters, the first editor has provided a moderately detailed summary of the formal results and the requisite mathematical concepts. These summaries are presented in four chapters that tie together the 16 contributed chapters: three develop a coherent view of the three general perspectives -- computational, dynamical, and statistical; the other assembles these three perspectives into a unified overview of the neural networks field.

Photonic Reservoir Computing

Author : Daniel Brunner
Publisher : Walter de Gruyter GmbH & Co KG
ISBN 13 : 3110583496
Total Pages : 276 pages
Book Rating : 4.1/5 (15 download)

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Book Synopsis Photonic Reservoir Computing by : Daniel Brunner

Download or read book Photonic Reservoir Computing written by Daniel Brunner and published by Walter de Gruyter GmbH & Co KG. This book was released on 2019-07-08 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt: Photonics has long been considered an attractive substrate for next generation implementations of machine-learning concepts. Reservoir Computing tremendously facilitated the realization of recurrent neural networks in analogue hardware. This concept exploits the properties of complex nonlinear dynamical systems, giving rise to photonic reservoirs implemented by semiconductor lasers, telecommunication modulators and integrated photonic chips.

Cellular Neural Networks and Visual Computing

Author : Leon O. Chua
Publisher : Cambridge University Press
ISBN 13 : 9780521018630
Total Pages : 412 pages
Book Rating : 4.0/5 (186 download)

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Book Synopsis Cellular Neural Networks and Visual Computing by : Leon O. Chua

Download or read book Cellular Neural Networks and Visual Computing written by Leon O. Chua and published by Cambridge University Press. This book was released on 2005-08-22 with total page 412 pages. Available in PDF, EPUB and Kindle. Book excerpt: Cellular Nonlinear/Neural Network (CNN) technology is both a revolutionary concept and an experimentally proven new computing paradigm. Analogic cellular computers based on CNNs are set to change the way analog signals are processed. This unique undergraduate level textbook includes many examples and exercises, including CNN simulator and development software accessible via the Internet. It is an ideal introduction to CNNs and analogic cellular computing for students, researchers and engineers from a wide range of disciplines. Leon Chua, co-inventor of the CNN, and Tamàs Roska are both highly respected pioneers in the field.

Handbook of Neural Computing Applications

Author : Alianna J. Maren
Publisher : Academic Press
ISBN 13 : 148326484X
Total Pages : 472 pages
Book Rating : 4.4/5 (832 download)

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Book Synopsis Handbook of Neural Computing Applications by : Alianna J. Maren

Download or read book Handbook of Neural Computing Applications written by Alianna J. Maren and published by Academic Press. This book was released on 2014-05-10 with total page 472 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Neural Network Parallel Computing

Author : Yoshiyasu Takefuji
Publisher : Springer Science & Business Media
ISBN 13 : 9780792391906
Total Pages : 254 pages
Book Rating : 4.3/5 (919 download)

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Book Synopsis Neural Network Parallel Computing by : Yoshiyasu Takefuji

Download or read book Neural Network Parallel Computing written by Yoshiyasu Takefuji and published by Springer Science & Business Media. This book was released on 1992-01-31 with total page 254 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neural Network Parallel Computing is the first book available to the professional market on neural network computing for optimization problems. This introductory book is not only for the novice reader, but for experts in a variety of areas including parallel computing, neural network computing, computer science, communications, graph theory, computer aided design for VLSI circuits, molecular biology, management science, and operations research. The goal of the book is to facilitate an understanding as to the uses of neural network models in real-world applications. Neural Network Parallel Computing presents a major breakthrough in science and a variety of engineering fields. The computational power of neural network computing is demonstrated by solving numerous problems such as N-queen, crossbar switch scheduling, four-coloring and k-colorability, graph planarization and channel routing, RNA secondary structure prediction, knight's tour, spare allocation, sorting and searching, and tiling. Neural Network Parallel Computing is an excellent reference for researchers in all areas covered by the book. Furthermore, the text may be used in a senior or graduate level course on the topic.

Using Artificial Neural Networks for Analog Integrated Circuit Design Automation

Author : João P. S. Rosa
Publisher : Springer Nature
ISBN 13 : 3030357430
Total Pages : 117 pages
Book Rating : 4.0/5 (33 download)

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Book Synopsis Using Artificial Neural Networks for Analog Integrated Circuit Design Automation by : João P. S. Rosa

Download or read book Using Artificial Neural Networks for Analog Integrated Circuit Design Automation written by João P. S. Rosa and published by Springer Nature. This book was released on 2019-12-11 with total page 117 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book addresses the automatic sizing and layout of analog integrated circuits (ICs) using deep learning (DL) and artificial neural networks (ANN). It explores an innovative approach to automatic circuit sizing where ANNs learn patterns from previously optimized design solutions. In opposition to classical optimization-based sizing strategies, where computational intelligence techniques are used to iterate over the map from devices’ sizes to circuits’ performances provided by design equations or circuit simulations, ANNs are shown to be capable of solving analog IC sizing as a direct map from specifications to the devices’ sizes. Two separate ANN architectures are proposed: a Regression-only model and a Classification and Regression model. The goal of the Regression-only model is to learn design patterns from the studied circuits, using circuit’s performances as input features and devices’ sizes as target outputs. This model can size a circuit given its specifications for a single topology. The Classification and Regression model has the same capabilities of the previous model, but it can also select the most appropriate circuit topology and its respective sizing given the target specification. The proposed methodology was implemented and tested on two analog circuit topologies.

Advances in Neural Information Processing Systems 9

Author : Michael C. Mozer
Publisher : MIT Press
ISBN 13 : 9780262100656
Total Pages : 1128 pages
Book Rating : 4.1/5 (6 download)

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Book Synopsis Advances in Neural Information Processing Systems 9 by : Michael C. Mozer

Download or read book Advances in Neural Information Processing Systems 9 written by Michael C. Mozer and published by MIT Press. This book was released on 1997 with total page 1128 pages. Available in PDF, EPUB and Kindle. Book excerpt: The annual conference on Neural Information Processing Systems (NIPS) is the flagship conference on neural computation. It draws preeminent academic researchers from around the world and is widely considered to be a showcase conference for new developments in network algorithms and architectures. The broad range of interdisciplinary research areas represented includes neural networks and genetic algorithms, cognitive science, neuroscience and biology, computer science, AI, applied mathematics, physics, and many branches of engineering. Only about 30% of the papers submitted are accepted for presentation at NIPS, so the quality is exceptionally high. All of the papers presented appear in these proceedings.