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2013 | OriginalPaper | Chapter

4. General Neural Networks

Authors : Rudolf Kruse, Christian Borgelt, Frank Klawonn, Christian Moewes, Matthias Steinbrecher, Pascal Held

Published in: Computational Intelligence

Publisher: Springer London

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Abstract

In this chapter we introduce a general model of (artificial) neural networks that captures (more or less) all special forms, which we consider in the following chapters. We start by defining the structure of an (artificial) neural network and then describe generally the operation and finally the training of an (artificial) neural network.

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previous chapter Threshold Logic Units
next chapter Multi-Layer Perceptrons
Footnotes
1
A loop is an edge/connection from a vertex to this vertex itself, that is, an edge e=(v,v) with a vertex v∈V.
 
2
A topological ordering is a numbering of the vertices of a directed graph, such that all edges are directed from a vertex with a lower number to a vertex with a higher number. A topological ordering exists only for acyclic graphs, that is, for feed forward networks. For feed forward networks, an update following a topological ordering ensures that all inputs of a neuron are already available (have already been computed), before it (re-)computes its own activation and output.
 
3
The second formula is based on the maximum likelihood estimator for the variance of a normal distribution. In statistics often the unbiased estimator is preferred, which differs from the one used above only by using |L|−1 instead of |L|. For the normalization this difference is negligible.
 
Metadata
Title
General Neural Networks
Authors
Rudolf Kruse
Christian Borgelt
Frank Klawonn
Christian Moewes
Matthias Steinbrecher
Pascal Held
Copyright Year
2013
Publisher
Springer London
Book
Computational Intelligence

Print ISBN: 978-1-4471-5012-1

Electronic ISBN: 978-1-4471-5013-8

Copyright Year: 2013

DOI
https://doi.org/10.1007/978-1-4471-5013-8_4

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