Book ChapterDOI
Recurrent neural networks
Hava T. Siegelmann
- pp 29-45
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TLDR
This paper reviews the recent findings that mathematically quantify the computational power and dynamic capabilities of recurrent neural networks.Abstract:
Neural networks have attracted much attention lately as a powerful tool of automatic learning. Of particular interest is the class of recurrent networks which allow for loops and cycles and thus give rise to dynamical systems, to flexible behavior, and to computation. This paper reviews the recent findings that mathematically quantify the computational power and dynamic capabilities of recurrent neural networks. The appeal of the network as a possible standard model of analog computation also will be discussed.read more
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Book ChapterDOI
Towards Machines That Can Think
TL;DR: A brief overview of related results from a machine oriented complexity theory about the study, design and realization of thinking machines is presented.
References
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Book
Neural Networks: A Comprehensive Foundation
TL;DR: Thorough, well-organized, and completely up to date, this book examines all the important aspects of this emerging technology, including the learning process, back-propagation learning, radial-basis function networks, self-organizing systems, modular networks, temporal processing and neurodynamics, and VLSI implementation of neural networks.
Journal ArticleDOI
A logical calculus of the ideas immanent in nervous activity
Warren S. McCulloch,Walter Pitts +1 more
TL;DR: In this article, it is shown that many particular choices among possible neurophysiological assumptions are equivalent, in the sense that for every net behaving under one assumption, there exists another net which behaves under another and gives the same results, although perhaps not in the same time.
Book
Introduction to Automata Theory, Languages, and Computation
TL;DR: This book is a rigorous exposition of formal languages and models of computation, with an introduction to computational complexity, appropriate for upper-level computer science undergraduates who are comfortable with mathematical arguments.
Journal ArticleDOI
Approximation by superpositions of a sigmoidal function
TL;DR: It is demonstrated that finite linear combinations of compositions of a fixed, univariate function and a set of affine functionals can uniformly approximate any continuous function ofn real variables with support in the unit hypercube.
Journal ArticleDOI
Approximation capabilities of multilayer feedforward networks
TL;DR: It is shown that standard multilayer feedforward networks with as few as a single hidden layer and arbitrary bounded and nonconstant activation function are universal approximators with respect to L p (μ) performance criteria, for arbitrary finite input environment measures μ.