Neural Networks And Qualitative Physics

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Neural Networks and Qualitative Physics

Author : Jean-Pierre Aubin
Publisher : Cambridge University Press
ISBN 13 : 9780521445320
Total Pages : 306 pages
Book Rating : 4.4/5 (453 download)

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Book Synopsis Neural Networks and Qualitative Physics by : Jean-Pierre Aubin

Download or read book Neural Networks and Qualitative Physics written by Jean-Pierre Aubin and published by Cambridge University Press. This book was released on 1996-03-29 with total page 306 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is devoted to some mathematical methods that arise in two domains of artificial intelligence: neural networks and qualitative physics. Professor Aubin makes use of control and viability theory in neural networks and cognitive systems, regarded as dynamical systems controlled by synaptic matrices, and set-valued analysis that plays a natural and crucial role in qualitative analysis and simulation. This allows many examples of neural networks to be presented in a unified way. In addition, several results on the control of linear and nonlinear systems are used to obtain a "learning algorithm" of pattern classification problems, such as the back-propagation formula, as well as learning algorithms of feedback regulation laws of solutions to control systems subject to state constraints.

Qualitative Analysis and Control of Complex Neural Networks with Delays

Author : Zhanshan Wang
Publisher : Springer
ISBN 13 : 3662474840
Total Pages : 398 pages
Book Rating : 4.6/5 (624 download)

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Book Synopsis Qualitative Analysis and Control of Complex Neural Networks with Delays by : Zhanshan Wang

Download or read book Qualitative Analysis and Control of Complex Neural Networks with Delays written by Zhanshan Wang and published by Springer. This book was released on 2015-07-18 with total page 398 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on the stability of the dynamical neural system, synchronization of the coupling neural system and their applications in automation control and electrical engineering. The redefined concept of stability, synchronization and consensus are adopted to provide a better explanation of the complex neural network. Researchers in the fields of dynamical systems, computer science, electrical engineering and mathematics will benefit from the discussions on complex systems. The book will also help readers to better understand the theory behind the control technique and its design.

Machine Learning with Neural Networks

Author : Bernhard Mehlig
Publisher : Cambridge University Press
ISBN 13 : 1108849563
Total Pages : 262 pages
Book Rating : 4.1/5 (88 download)

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Book Synopsis Machine Learning with Neural Networks by : Bernhard Mehlig

Download or read book Machine Learning with Neural Networks written by Bernhard Mehlig and published by Cambridge University Press. This book was released on 2021-10-28 with total page 262 pages. Available in PDF, EPUB and Kindle. Book excerpt: This modern and self-contained book offers a clear and accessible introduction to the important topic of machine learning with neural networks. In addition to describing the mathematical principles of the topic, and its historical evolution, strong connections are drawn with underlying methods from statistical physics and current applications within science and engineering. Closely based around a well-established undergraduate course, this pedagogical text provides a solid understanding of the key aspects of modern machine learning with artificial neural networks, for students in physics, mathematics, and engineering. Numerous exercises expand and reinforce key concepts within the book and allow students to hone their programming skills. Frequent references to current research develop a detailed perspective on the state-of-the-art in machine learning research.

Statistical Mechanics of Neural Networks

Author : Haiping Huang
Publisher : Springer Nature
ISBN 13 : 9811675708
Total Pages : 302 pages
Book Rating : 4.8/5 (116 download)

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Book Synopsis Statistical Mechanics of Neural Networks by : Haiping Huang

Download or read book Statistical Mechanics of Neural Networks written by Haiping Huang and published by Springer Nature. This book was released on 2022-01-04 with total page 302 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book highlights a comprehensive introduction to the fundamental statistical mechanics underneath the inner workings of neural networks. The book discusses in details important concepts and techniques including the cavity method, the mean-field theory, replica techniques, the Nishimori condition, variational methods, the dynamical mean-field theory, unsupervised learning, associative memory models, perceptron models, the chaos theory of recurrent neural networks, and eigen-spectrums of neural networks, walking new learners through the theories and must-have skillsets to understand and use neural networks. The book focuses on quantitative frameworks of neural network models where the underlying mechanisms can be precisely isolated by physics of mathematical beauty and theoretical predictions. It is a good reference for students, researchers, and practitioners in the area of neural networks.

Neural Networks

Author :
Publisher :
ISBN 13 :
Total Pages : 30 pages
Book Rating : 4.:/5 (12 download)

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Book Synopsis Neural Networks by :

Download or read book Neural Networks written by and published by . This book was released on with total page 30 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides the first accessible introduction to neural network analysis as a methodological strategy for social scientists. The author details numerous studies and examples which illustrate the advantages of neural network analysis over other quantitative and modelling methods in widespread use. Methods are presented in an accessible style for readers who do not have a background in computer science. The book provides a history of neural network methods, a substantial review of the literature, detailed applications, coverage of the most common alternative models and examples of two leading software packages for neural network analysis.

Deep Learning For Physics Research

Author : Martin Erdmann
Publisher : World Scientific
ISBN 13 : 9811237476
Total Pages : 340 pages
Book Rating : 4.8/5 (112 download)

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Book Synopsis Deep Learning For Physics Research by : Martin Erdmann

Download or read book Deep Learning For Physics Research written by Martin Erdmann and published by World Scientific. This book was released on 2021-06-25 with total page 340 pages. Available in PDF, EPUB and Kindle. Book excerpt: A core principle of physics is knowledge gained from data. Thus, deep learning has instantly entered physics and may become a new paradigm in basic and applied research.This textbook addresses physics students and physicists who want to understand what deep learning actually means, and what is the potential for their own scientific projects. Being familiar with linear algebra and parameter optimization is sufficient to jump-start deep learning. Adopting a pragmatic approach, basic and advanced applications in physics research are described. Also offered are simple hands-on exercises for implementing deep networks for which python code and training data can be downloaded.

Statistical Field Theory for Neural Networks

Author : Moritz Helias
Publisher : Springer Nature
ISBN 13 : 303046444X
Total Pages : 203 pages
Book Rating : 4.0/5 (34 download)

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Book Synopsis Statistical Field Theory for Neural Networks by : Moritz Helias

Download or read book Statistical Field Theory for Neural Networks written by Moritz Helias and published by Springer Nature. This book was released on 2020-08-20 with total page 203 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a self-contained introduction to techniques from field theory applied to stochastic and collective dynamics in neuronal networks. These powerful analytical techniques, which are well established in other fields of physics, are the basis of current developments and offer solutions to pressing open problems in theoretical neuroscience and also machine learning. They enable a systematic and quantitative understanding of the dynamics in recurrent and stochastic neuronal networks. This book is intended for physicists, mathematicians, and computer scientists and it is designed for self-study by researchers who want to enter the field or as the main text for a one semester course at advanced undergraduate or graduate level. The theoretical concepts presented in this book are systematically developed from the very beginning, which only requires basic knowledge of analysis and linear algebra.

The Principles of Deep Learning Theory

Author : Daniel A. Roberts
Publisher : Cambridge University Press
ISBN 13 : 1316519333
Total Pages : 473 pages
Book Rating : 4.3/5 (165 download)

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Book Synopsis The Principles of Deep Learning Theory by : Daniel A. Roberts

Download or read book The Principles of Deep Learning Theory written by Daniel A. Roberts and published by Cambridge University Press. This book was released on 2022-05-26 with total page 473 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume develops an effective theory approach to understanding deep neural networks of practical relevance.

Neural Networks: From Biology To High Energy Physics - Proceedings Of The Third Workshop

Author : Daniel J Amit
Publisher : World Scientific
ISBN 13 : 9814548405
Total Pages : 310 pages
Book Rating : 4.8/5 (145 download)

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Book Synopsis Neural Networks: From Biology To High Energy Physics - Proceedings Of The Third Workshop by : Daniel J Amit

Download or read book Neural Networks: From Biology To High Energy Physics - Proceedings Of The Third Workshop written by Daniel J Amit and published by World Scientific. This book was released on 1995-10-18 with total page 310 pages. Available in PDF, EPUB and Kindle. Book excerpt: The papers appearing in this proceedings volume cover a broad range of subjects, owing to the highly cross-disciplinary character of the workshop, and include: experiments and models concerning the dynamics of the neural activity in the cortex (DMS experiments, attractor dynamics in the cortex, spontaneous activity…); hippocampus, space and memory; theoretical advances in neural network modeling; information processing in neural networks; applications of neural networks to experimental physics, particularly to high energy physics; digital and analog hardware implementations of neural networks; etc.

An Introduction To The Theory Of Spin Glasses And Neural Networks

Author : V Dotsenko
Publisher : World Scientific
ISBN 13 : 9814501654
Total Pages : 166 pages
Book Rating : 4.8/5 (145 download)

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Book Synopsis An Introduction To The Theory Of Spin Glasses And Neural Networks by : V Dotsenko

Download or read book An Introduction To The Theory Of Spin Glasses And Neural Networks written by V Dotsenko and published by World Scientific. This book was released on 1995-01-16 with total page 166 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book aims to describe in simple terms the new area of statistical mechanics known as spin-glasses, encompassing systems in which quenched disorder is the dominant factor. The book begins with a non-mathematical explanation of the problem, and the modern understanding of the physics of the spin-glass state is formulated in general terms. Next, the 'magic' of the replica symmetry breaking scheme is demonstrated and the physics behind it discussed. Recent experiments on real spin-glass materials are briefly described to demonstrate how this somewhat abstract physics can be studied in the laboratory. The final chapters of the book are devoted to statistical models of neural networks.The material here is self-contained and should be accessible to students with a basic knowledge of theoretical physics and statistical mechanics. It has been used for a one-term graduate lecture course at the Landau Institute for Theoretical Physics.

Deep Learning and Physics

Author : Akinori Tanaka
Publisher : Springer Nature
ISBN 13 : 9813361085
Total Pages : 207 pages
Book Rating : 4.8/5 (133 download)

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Book Synopsis Deep Learning and Physics by : Akinori Tanaka

Download or read book Deep Learning and Physics written by Akinori Tanaka and published by Springer Nature. This book was released on 2021-03-24 with total page 207 pages. Available in PDF, EPUB and Kindle. Book excerpt: What is deep learning for those who study physics? Is it completely different from physics? Or is it similar? In recent years, machine learning, including deep learning, has begun to be used in various physics studies. Why is that? Is knowing physics useful in machine learning? Conversely, is knowing machine learning useful in physics? This book is devoted to answers of these questions. Starting with basic ideas of physics, neural networks are derived naturally. And you can learn the concepts of deep learning through the words of physics. In fact, the foundation of machine learning can be attributed to physical concepts. Hamiltonians that determine physical systems characterize various machine learning structures. Statistical physics given by Hamiltonians defines machine learning by neural networks. Furthermore, solving inverse problems in physics through machine learning and generalization essentially provides progress and even revolutions in physics. For these reasons, in recent years interdisciplinary research in machine learning and physics has been expanding dramatically. This book is written for anyone who wants to learn, understand, and apply the relationship between deep learning/machine learning and physics. All that is needed to read this book are the basic concepts in physics: energy and Hamiltonians. The concepts of statistical mechanics and the bracket notation of quantum mechanics, which are explained in columns, are used to explain deep learning frameworks. We encourage you to explore this new active field of machine learning and physics, with this book as a map of the continent to be explored.

Neural-Network Simulation of Strongly Correlated Quantum Systems

Author : Stefanie Czischek
Publisher : Springer Nature
ISBN 13 : 3030527158
Total Pages : 205 pages
Book Rating : 4.0/5 (35 download)

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Book Synopsis Neural-Network Simulation of Strongly Correlated Quantum Systems by : Stefanie Czischek

Download or read book Neural-Network Simulation of Strongly Correlated Quantum Systems written by Stefanie Czischek and published by Springer Nature. This book was released on 2020-08-27 with total page 205 pages. Available in PDF, EPUB and Kindle. Book excerpt: Quantum systems with many degrees of freedom are inherently difficult to describe and simulate quantitatively. The space of possible states is, in general, exponentially large in the number of degrees of freedom such as the number of particles it contains. Standard digital high-performance computing is generally too weak to capture all the necessary details, such that alternative quantum simulation devices have been proposed as a solution. Artificial neural networks, with their high non-local connectivity between the neuron degrees of freedom, may soon gain importance in simulating static and dynamical behavior of quantum systems. Particularly promising candidates are neuromorphic realizations based on analog electronic circuits which are being developed to capture, e.g., the functioning of biologically relevant networks. In turn, such neuromorphic systems may be used to measure and control real quantum many-body systems online. This thesis lays an important foundation for the realization of quantum simulations by means of neuromorphic hardware, for using quantum physics as an input to classical neural nets and, in turn, for using network results to be fed back to quantum systems. The necessary foundations on both sides, quantum physics and artificial neural networks, are described, providing a valuable reference for researchers from these different communities who need to understand the foundations of both.

An Introduction to the Theory of Spin Glasses and Neural Networks

Author : Viktor Dotsenko
Publisher : World Scientific
ISBN 13 : 9789810218737
Total Pages : 172 pages
Book Rating : 4.2/5 (187 download)

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Book Synopsis An Introduction to the Theory of Spin Glasses and Neural Networks by : Viktor Dotsenko

Download or read book An Introduction to the Theory of Spin Glasses and Neural Networks written by Viktor Dotsenko and published by World Scientific. This book was released on 1994 with total page 172 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book aims to describe in simple terms the new area of statistical mechanics known as spin-glasses, encompassing systems in which quenched disorder is the dominant factor. The book begins with a non-mathematical explanation of the problem, and the modern understanding of the physics of the spin-glass state is formulated in general terms. Next, the 'magic' of the replica symmetry breaking scheme is demonstrated and the physics behind it discussed. Recent experiments on real spin-glass materials are briefly described to demonstrate how this somewhat abstract physics can be studied in the laboratory. The final chapters of the book are devoted to statistical models of neural networks.The material here is self-contained and should be accessible to students with a basic knowledge of theoretical physics and statistical mechanics. It has been used for a one-term graduate lecture course at the Landau Institute for Theoretical Physics.

Artificial Neural Networks for Modelling and Control of Non-Linear Systems

Author : Johan A.K. Suykens
Publisher : Springer Science & Business Media
ISBN 13 : 9780792396789
Total Pages : 258 pages
Book Rating : 4.3/5 (967 download)

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Book Synopsis Artificial Neural Networks for Modelling and Control of Non-Linear Systems by : Johan A.K. Suykens

Download or read book Artificial Neural Networks for Modelling and Control of Non-Linear Systems written by Johan A.K. Suykens and published by Springer Science & Business Media. This book was released on 1995-12-31 with total page 258 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial neural networks possess several properties that make them particularly attractive for applications to modelling and control of complex non-linear systems. Among these properties are their universal approximation ability, their parallel network structure and the availability of on- and off-line learning methods for the interconnection weights. However, dynamic models that contain neural network architectures might be highly non-linear and difficult to analyse as a result. Artificial Neural Networks for Modelling and Control of Non-Linear Systems investigates the subject from a system theoretical point of view. However the mathematical theory that is required from the reader is limited to matrix calculus, basic analysis, differential equations and basic linear system theory. No preliminary knowledge of neural networks is explicitly required. The book presents both classical and novel network architectures and learning algorithms for modelling and control. Topics include non-linear system identification, neural optimal control, top-down model based neural control design and stability analysis of neural control systems. A major contribution of this book is to introduce NLq Theory as an extension towards modern control theory, in order to analyze and synthesize non-linear systems that contain linear together with static non-linear operators that satisfy a sector condition: neural state space control systems are an example. Moreover, it turns out that NLq Theory is unifying with respect to many problems arising in neural networks, systems and control. Examples show that complex non-linear systems can be modelled and controlled within NLq theory, including mastering chaos. The didactic flavor of this book makes it suitable for use as a text for a course on Neural Networks. In addition, researchers and designers will find many important new techniques, in particular NLq emTheory, that have applications in control theory, system theory, circuit theory and Time Series Analysis.

Neural Networks: From Biology To High Energy Physics - Proceedings Of The 2nd Workshop

Author : Omar Benhar
Publisher : World Scientific
ISBN 13 : 9814553581
Total Pages : 358 pages
Book Rating : 4.8/5 (145 download)

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Book Synopsis Neural Networks: From Biology To High Energy Physics - Proceedings Of The 2nd Workshop by : Omar Benhar

Download or read book Neural Networks: From Biology To High Energy Physics - Proceedings Of The 2nd Workshop written by Omar Benhar and published by World Scientific. This book was released on 1993-10-16 with total page 358 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neural network models, in addition to being of intrinsic theoretical interest, have also proved to be a useful framework in which issues in theoretical biology can be put into perspective. These issues include, amongst others, modelling the activity of the cortex and the study of protein folding. More recently, neural network models have been extensively investigated as tools for data analysis in high energy physics experiments. These workshop proceedings reflect the strongly interdisciplinary character of the field and provide an updated overview of recent developments.

Applications of Mathematics in Models, Artificial Neural Networks and Arts

Author : Vittorio Capecchi
Publisher : Springer Science & Business Media
ISBN 13 : 9048185815
Total Pages : 616 pages
Book Rating : 4.0/5 (481 download)

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Book Synopsis Applications of Mathematics in Models, Artificial Neural Networks and Arts by : Vittorio Capecchi

Download or read book Applications of Mathematics in Models, Artificial Neural Networks and Arts written by Vittorio Capecchi and published by Springer Science & Business Media. This book was released on 2010-08-03 with total page 616 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book shows a very original organization addressing in a non traditional way, but with a systematic approach, to who has an interest in using mathematics in the social sciences. The book is divided in four parts: (a) a historical part, written by Vittorio Capecchi which helps us understand the changes in the relationship between mathematics and sociology by analyzing the mathematical models of Paul F. Lazarsfeld, the model of simulation and artificial societies, models of artificial neural network and considering all the changes in scientific paradigms considered; (b) a part coordinated by Pier Luigi Contucci on mathematical models that consider the relationship between the mathematical models that come from physics and linguistics to arrive at the study of society and those which are born within sociology and economics; (c) a part coordinated by Massimo Buscema analyzing models of artificial neural networks; (d) a part coordinated by Bruno D’Amore which considers the relationship between mathematics and art. The title of the book "Mathematics and Society" was chosen because the mathematical applications exposed in the book allow you to address two major issues: (a) the general theme of technological innovation and quality of life (among the essays are on display mathematical applications to the problems of combating pollution and crime, applications to mathematical problems of immigration, mathematical applications to the problems of medical diagnosis, etc.) (b) the general theme of technical innovation and creativity, for example the art and mathematics section which connects to the theme of creative cities. The book is very original because it is not addressed only to those who are passionate about mathematical applications in social science but also to those who, in different societies, are: (a) involved in technological innovation to improve the quality of life; (b) involved in the wider distribution of technological innovation in different areas of creativity (as in the project "Creative Cities Network" of UNESCO).

Neural Networks and Soft Computing

Author : Leszek Rutkowski
Publisher : Springer Science & Business Media
ISBN 13 : 3790819026
Total Pages : 935 pages
Book Rating : 4.7/5 (98 download)

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Book Synopsis Neural Networks and Soft Computing by : Leszek Rutkowski

Download or read book Neural Networks and Soft Computing written by Leszek Rutkowski and published by Springer Science & Business Media. This book was released on 2013-03-20 with total page 935 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume presents new trends and developments in soft computing techniques. Topics include: neural networks, fuzzy systems, evolutionary computation, knowledge discovery, rough sets, and hybrid methods. It also covers various applications of soft computing techniques in economics, mechanics, medicine, automatics and image processing. The book contains contributions from internationally recognized scientists, such as Zadeh, Bubnicki, Pawlak, Amari, Batyrshin, Hirota, Koczy, Kosinski, Novák, S.-Y. Lee, Pedrycz, Raudys, Setiono, Sincak, Strumillo, Takagi, Usui, Wilamowski and Zurada. An excellent overview of soft computing methods and their applications.