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Neural Networks and Analog Computation (Progress in Theoretical Computer Science)

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Neural Networks and Analog Computation (Progress in Theoretical Computer Science) Cover

 

Synopses & Reviews

Publisher Comments:

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. What emerges is a Church-Turing-like thesis, applied to the field of analog computation, which features the neural network model in place of the digital Turing machine. This new concept can serve as a point of departure for the development of alternative, supra-Turing, computational theories. 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 topics covered in this work will appeal to a wide readership from a variety of disciplines. Special care has been taken to explain the theory clearly and concisely. The first chapter review s the fundamental terms of modern computational theory from the point of view of neural networks and serves as a reference for the remainder of the book. Each of the subsequent chapters opens with introductory material and proceeds to explain the chapter's connection to the development of the theory. Thereafter, the concept is defined in mathematical terms. Although the notion of a neural network essentially arises from biology, many engineering applications have been found through highly idealized and simplified models of neuron behavior. Particular areas of application have been as diverse as explosives detection in airport security, signature verification, financial and medical times series prediction, vision, speech processing, robotics, nonlinear control, and signal processing. The focus in all of these models is entirely on the behavior of networks as computer. 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.

Synopsis:

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.

Table of Contents

Introduction.-Computational Complexity.-The Model.-Networks with Rational Weights.-Networks with Real Weights.-Kolmogorov Weights: Between P and P/poly.-Space and Precision.-Universality of Sigmoidal Networks.-Different-limits Networks.-Stochastic Dynamics.-Generalized Processor Networks.-Analog Computation.-Computation beyond the Turing Limit.-Bibliography.-Index.

Product Details

ISBN:
9780817639495
Author:
Siegelman, Hava
Author:
Siegelman
Author:
Siegelman, Hava T.
Author:
Siegelmann, Hava T.
Publisher:
Birkhauser
Location:
Boston, Mass. :
Subject:
Computer Science
Subject:
Engineering - General
Subject:
Artificial Intelligence
Subject:
Mathematical Physics
Subject:
Neural Networks
Subject:
Neural networks (computer science)
Subject:
Computational complexity
Subject:
NETWORKS
Subject:
Artificial Intelligence - General
Subject:
Intelligence (AI) & Semantics
Subject:
Math Applications in Computer Science
Subject:
APPLICATIONS OF MATHEMATICS
Subject:
Statistical Physics, Dynamical Systems and Complexity
Subject:
Artificial Intelligence (incl. Robotics)
Subject:
Appl.Mathematics/Computational Methods of Engineering
Subject:
Theory of computation
Subject:
Networking - General
Subject:
Data processing
Copyright:
Edition Number:
1
Edition Description:
Book
Series:
Progress in Theoretical Computer Science
Publication Date:
19981201
Binding:
HARDCOVER
Language:
English
Illustrations:
Yes
Pages:
195
Dimensions:
235 x 155 mm 1030 gr

Related Subjects

Business » Communication
Business » General
Computers and Internet » Artificial Intelligence » General
Computers and Internet » Computers Reference » General
Computers and Internet » Networking » General
Education » Phonics
Science and Mathematics » Mathematics » Applied
Science and Mathematics » Mathematics » Differential Equations
Science and Mathematics » Physics » Math

Neural Networks and Analog Computation (Progress in Theoretical Computer Science) New Hardcover
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Product details 195 pages Birkhauser Boston - English 9780817639495 Reviews:
"Synopsis" by , 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.
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