Neural Networks Theory PDF Download
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Author | : Alexander I. Galushkin |
Publisher | : Springer Science & Business Media |
Total Pages | : 396 |
Release | : 2007-10-29 |
Genre | : Technology & Engineering |
ISBN | : 3540481257 |
Download Neural Networks Theory Book in PDF, ePub and Kindle
This book, written by a leader in neural network theory in Russia, uses mathematical methods in combination with complexity theory, nonlinear dynamics and optimization. It details more than 40 years of Soviet and Russian neural network research and presents a systematized methodology of neural networks synthesis. The theory is expansive: covering not just traditional topics such as network architecture but also neural continua in function spaces as well.
Author | : Daniel A. Roberts |
Publisher | : Cambridge University Press |
Total Pages | : 473 |
Release | : 2022-05-26 |
Genre | : Computers |
ISBN | : 1316519333 |
Download The Principles of Deep Learning Theory Book in PDF, ePub and Kindle
This volume develops an effective theory approach to understanding deep neural networks of practical relevance.
Author | : Xingui He |
Publisher | : Springer Science & Business Media |
Total Pages | : 240 |
Release | : 2010-07-05 |
Genre | : Computers |
ISBN | : 3540737626 |
Download Process Neural Networks Book in PDF, ePub and Kindle
For the first time, this book sets forth the concept and model for a process neural network. You’ll discover how a process neural network expands the mapping relationship between the input and output of traditional neural networks and greatly enhances the expression capability of artificial neural networks. Detailed illustrations help you visualize information processing flow and the mapping relationship between inputs and outputs.
Author | : Martin Anthony |
Publisher | : Cambridge University Press |
Total Pages | : 405 |
Release | : 1999-11-04 |
Genre | : Computers |
ISBN | : 052157353X |
Download Neural Network Learning Book in PDF, ePub and Kindle
This work explores probabilistic models of supervised learning problems and addresses the key statistical and computational questions. Chapters survey research on pattern classification with binary-output networks, including a discussion of the relevance of the Vapnik Chervonenkis dimension, and of estimates of the dimension for several neural network models. In addition, the authors develop a model of classification by real-output networks, and demonstrate the usefulness of classification...
Author | : Michael A. Arbib |
Publisher | : MIT Press |
Total Pages | : 1328 |
Release | : 2003 |
Genre | : Neural circuitry |
ISBN | : 0262011972 |
Download The Handbook of Brain Theory and Neural Networks Book in PDF, ePub and Kindle
This second edition presents the enormous progress made in recent years in the many subfields related to the two great questions : how does the brain work? and, How can we build intelligent machines? This second edition greatly increases the coverage of models of fundamental neurobiology, cognitive neuroscience, and neural network approaches to language. (Midwest).
Author | : Seyedali Mirjalili |
Publisher | : Springer |
Total Pages | : 156 |
Release | : 2018-06-26 |
Genre | : Technology & Engineering |
ISBN | : 3319930257 |
Download Evolutionary Algorithms and Neural Networks Book in PDF, ePub and Kindle
This book introduces readers to the fundamentals of artificial neural networks, with a special emphasis on evolutionary algorithms. At first, the book offers a literature review of several well-regarded evolutionary algorithms, including particle swarm and ant colony optimization, genetic algorithms and biogeography-based optimization. It then proposes evolutionary version of several types of neural networks such as feed forward neural networks, radial basis function networks, as well as recurrent neural networks and multi-later perceptron. Most of the challenges that have to be addressed when training artificial neural networks using evolutionary algorithms are discussed in detail. The book also demonstrates the application of the proposed algorithms for several purposes such as classification, clustering, approximation, and prediction problems. It provides a tutorial on how to design, adapt, and evaluate artificial neural networks as well, and includes source codes for most of the proposed techniques as supplementary materials.
Author | : K. I. Diamantaras |
Publisher | : Wiley-Interscience |
Total Pages | : 282 |
Release | : 1996-03-08 |
Genre | : Computers |
ISBN | : |
Download Principal Component Neural Networks Book in PDF, ePub and Kindle
Systematically explores the relationship between principal component analysis (PCA) and neural networks. Provides a synergistic examination of the mathematical, algorithmic, application and architectural aspects of principal component neural networks. Using a unified formulation, the authors present neural models performing PCA from the Hebbian learning rule and those which use least squares learning rules such as back-propagation. Examines the principles of biological perceptual systems to explain how the brain works. Every chapter contains a selected list of applications examples from diverse areas.
Author | : John A. Hertz |
Publisher | : CRC Press |
Total Pages | : 352 |
Release | : 2018-03-08 |
Genre | : Science |
ISBN | : 0429968213 |
Download Introduction To The Theory Of Neural Computation Book in PDF, ePub and Kindle
Comprehensive introduction to the neural network models currently under intensive study for computational applications. It also provides coverage of neural network applications in a variety of problems of both theoretical and practical interest.
Author | : Madan Gupta |
Publisher | : John Wiley & Sons |
Total Pages | : 752 |
Release | : 2004-04-05 |
Genre | : Computers |
ISBN | : 0471460923 |
Download Static and Dynamic Neural Networks Book in PDF, ePub and Kindle
Neuronale Netze haben sich in vielen Bereichen der Informatik und künstlichen Intelligenz, der Robotik, Prozeßsteuerung und Entscheidungsfindung bewährt. Um solche Netze für immer komplexere Aufgaben entwickeln zu können, benötigen Sie solide Kenntnisse der Theorie statischer und dynamischer neuronaler Netze. Aneignen können Sie sie sich mit diesem Lehrbuch! Alle theoretischen Konzepte sind in anschaulicher Weise mit praktischen Anwendungen verknüpft. Am Ende jedes Kapitels können Sie Ihren Wissensstand anhand von Übungsaufgaben überprüfen.
Author | : Moritz Helias |
Publisher | : Springer Nature |
Total Pages | : 203 |
Release | : 2020-08-20 |
Genre | : Science |
ISBN | : 303046444X |
Download Statistical Field Theory for Neural Networks Book in PDF, ePub and Kindle
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.