Java and Web Technologies

Satisfactory Essays
Topics: Linear algebra
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NEURAL NETWORKS

Ivan F Wilde
Mathematics Department
King’s College London
London, WC2R 2LS, UK

ivan.wilde@kcl.ac.uk

www.jntuworld.com

www.jntuworld.com

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Contents

1 Matrix Memory . . . . . . . . . . . . . . . . . . . . . . . . . .

1

2 Adaptive Linear Combiner

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21

3 Artificial Neural Networks

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35

..........................

45

5 Multilayer Feedforward Networks . . . . . . . . . . . . . . . . .

75

6 Radial Basis Functions

95

4 The Perceptron

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7 Recurrent Neural Networks . . . . . . . . . . . . . . . . . . . . 103
8 Singular Value Decomposition
Bibliography

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. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121

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Chapter 1
Matrix Memory

We wish to construct a system which possesses so-called associative memory.
This is definable generally as a process by which an input, considered as a
“key”, to a memory system is able to evoke, in a highly selective fashion, a specific response associated with that key, at the system output. The signalresponse association should be “robust”, that is, a “noisy” or “incomplete” input signal should none the less invoke the correct response—or at least an acceptable response. Such a system is also called a content addressable memory. 3

mapping

stimulus

response

Figure 1.1: A content addressable memory.
The idea is that the association should not be defined so much between the individual stimulus-response pairs, but rather embodied as a whole collection of such input-output patterns—the system is a distributive associative memory (the input-output pairs are “distributed” throughout the system memory rather than the particular input-output pairs being somehow represented individually in various different parts of the

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