Neurodynamics - Proceedings Of The 9th Summer Workshop PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Neurodynamics - Proceedings Of The 9th Summer Workshop PDF full book. Access full book title Neurodynamics - Proceedings Of The 9th Summer Workshop by Heinz-dietrich Doebner. Download full books in PDF and EPUB format.
Author: Heinz-dietrich Doebner
Publisher: World Scientific
ISBN: 9814555541
Category :
Languages : en
Pages : 246
Get Book Here
Book Description
This volume presents applications of mathematical techniques for modelling and performance analysis of neural networks. The collection of articles is motivated by the observation that the theory of neural network dynamics, i.e. Neurodynamics, still has to be given a thorough mathematical foundation. Therefore, the volume comprises research work on different mathematical approaches to neural networks; analytical and numerical techniques of dynamical systems theory, geometrical techniques, and methods of statistical physics. Articles analyse dynamics of neural netwroks in general or concentrate on specific network models of biological or neurocomputing origin. A few of the articles serve as a good introduction to these subjects.
Author: Heinz-dietrich Doebner
Publisher: World Scientific
ISBN: 9814555541
Category :
Languages : en
Pages : 246
Get Book Here
Book Description
This volume presents applications of mathematical techniques for modelling and performance analysis of neural networks. The collection of articles is motivated by the observation that the theory of neural network dynamics, i.e. Neurodynamics, still has to be given a thorough mathematical foundation. Therefore, the volume comprises research work on different mathematical approaches to neural networks; analytical and numerical techniques of dynamical systems theory, geometrical techniques, and methods of statistical physics. Articles analyse dynamics of neural netwroks in general or concentrate on specific network models of biological or neurocomputing origin. A few of the articles serve as a good introduction to these subjects.
Author: John Milton
Publisher: Springer Nature
ISBN: 3030695794
Category : Mathematics
Languages : en
Pages : 650
Get Book Here
Book Description
The second edition of Mathematics as a Laboratory Tool reflects the growing impact that computational science is having on the career choices made by undergraduate science and engineering students. The focus is on dynamics and the effects of time delays and stochastic perturbations (“noise”) on the regulation provided by feedback control systems. The concepts are illustrated with applications to gene regulatory networks, motor control, neuroscience and population biology. The presentation in the first edition has been extended to include discussions of neuronal excitability and bursting, multistability, microchaos, Bayesian inference, second-order delay differential equations, and the semi-discretization method for the numerical integration of delay differential equations. Every effort has been made to ensure that the material is accessible to those with a background in calculus. The text provides advanced mathematical concepts such as the Laplace and Fourier integral transforms in the form of Tools. Bayesian inference is introduced using a number of detective-type scenarios including the Monty Hall problem.
Author: Paul Smolensky
Publisher: Psychology Press
ISBN: 1134772947
Category : Psychology
Languages : en
Pages : 865
Get Book Here
Book Description
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.
Author:
Publisher:
ISBN:
Category : Malaysian literature (English)
Languages : en
Pages : 644
Get Book Here
Book Description
Author:
Publisher:
ISBN:
Category : Conference proceedings
Languages : en
Pages : 856
Get Book Here
Book Description
Author:
Publisher:
ISBN:
Category : Engineering
Languages : en
Pages : 836
Get Book Here
Book Description
Author: Simon Haykin
Publisher:
ISBN: 9788178083001
Category : Neural networks (Computer science)
Languages : en
Pages : 842
Get Book Here
Book Description
Author: Martin T. Hagan
Publisher:
ISBN: 9789812403766
Category : Neural networks (Computer science)
Languages : en
Pages :
Get Book Here
Book Description
Author: Andrzej Cichocki
Publisher: John Wiley & Sons
ISBN:
Category : Computers
Languages : en
Pages : 578
Get Book Here
Book Description
A topical introduction on the ability of artificial neural networks to not only solve on-line a wide range of optimization problems but also to create new techniques and architectures. Provides in-depth coverage of mathematical modeling along with illustrative computer simulation results.
Author: Jude W. Shavlik
Publisher: Morgan Kaufmann
ISBN: 9781558601437
Category : Computers
Languages : en
Pages : 868
Get Book Here
Book Description
The ability to learn is a fundamental characteristic of intelligent behavior. Consequently, machine learning has been a focus of artificial intelligence since the beginnings of AI in the 1950s. The 1980s saw tremendous growth in the field, and this growth promises to continue with valuable contributions to science, engineering, and business. Readings in Machine Learning collects the best of the published machine learning literature, including papers that address a wide range of learning tasks, and that introduce a variety of techniques for giving machines the ability to learn. The editors, in cooperation with a group of expert referees, have chosen important papers that empirically study, theoretically analyze, or psychologically justify machine learning algorithms. The papers are grouped into a dozen categories, each of which is introduced by the editors.
