Neural Networks Theory
Title | Neural Networks Theory PDF eBook |
Author | Alexander I. Galushkin |
Publisher | Springer Science & Business Media |
Total Pages | 396 |
Release | 2007-10-29 |
Genre | Technology & Engineering |
ISBN | 3540481257 |
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.
The Principles of Deep Learning Theory
Title | The Principles of Deep Learning Theory PDF eBook |
Author | Daniel A. Roberts |
Publisher | Cambridge University Press |
Total Pages | 473 |
Release | 2022-05-26 |
Genre | Computers |
ISBN | 1316519333 |
This volume develops an effective theory approach to understanding deep neural networks of practical relevance.
Process Neural Networks
Title | Process Neural Networks PDF eBook |
Author | Xingui He |
Publisher | Springer Science & Business Media |
Total Pages | 240 |
Release | 2010-07-05 |
Genre | Computers |
ISBN | 3540737626 |
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.
The Handbook of Brain Theory and Neural Networks
Title | The Handbook of Brain Theory and Neural Networks PDF eBook |
Author | Michael A. Arbib |
Publisher | MIT Press |
Total Pages | 1328 |
Release | 2003 |
Genre | Neural circuitry |
ISBN | 0262011972 |
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).
Neural Network Learning
Title | Neural Network Learning PDF eBook |
Author | Martin Anthony |
Publisher | Cambridge University Press |
Total Pages | 405 |
Release | 1999-11-04 |
Genre | Computers |
ISBN | 052157353X |
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...
Introduction To The Theory Of Neural Computation
Title | Introduction To The Theory Of Neural Computation PDF eBook |
Author | John A. Hertz |
Publisher | CRC Press |
Total Pages | 352 |
Release | 2018-03-08 |
Genre | Science |
ISBN | 0429968213 |
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.
Evolutionary Algorithms and Neural Networks
Title | Evolutionary Algorithms and Neural Networks PDF eBook |
Author | Seyedali Mirjalili |
Publisher | Springer |
Total Pages | 156 |
Release | 2018-06-26 |
Genre | Technology & Engineering |
ISBN | 3319930257 |
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.