Veelenturf – Analysis And Applications of Artificial Neural

Table of contents :
Preface……Page 5
Acknowledgements……Page 7
Machines and brains……Page 8
The artificial neural network……Page 9
Introduction……Page 11
The performance of a single-neuron binary Perceptron……Page 14
Equivalent linear treshold function……Page 18
Learning a single-neuron binary Perceptron with the reinforcement rule……Page 20
The perceptron convergence theorem……Page 24
Performance of a two-layer binary Perceptron……Page 25
The adaptive recruitment learning rule……Page 30
Generalizing with a two-layer binary Perceptron……Page 32
The recruitment and reinforcement learning rule……Page 34
Application of the adaptive recruitment learning rule to switch circuits……Page 37
Application of the adaptive recruitment learning rule to hyphenation……Page 38
Application of the recruitment and reinforcement learning rule to contradictory binary data sets……Page 39
Intruduction……Page 41
The gradient descent adaptation method……Page 43
Learning with a single-neuron continous Perceptron……Page 46
The exacpt fitting of the data set with a single-neuron perceptron……Page 48
The approximate fiting of the data set with a single-neuron Perceptron……Page 50
Generalizing with a single-neuron continous Perceptron……Page 53
The classification of data with a single-neuron Perceptron……Page 54
Hyperplane boundary classification by one-zero labelling……Page 56
Hyperplane boundary classification by double treshold labelling……Page 61
Hyperplane boundary classification by single treshold labelling……Page 64
Application to the calssification of normally distributed classes……Page 68
Learning rule for a two-layer continuous Perceptron……Page 69
Under-fitting and over-fitting of a data set with a two-layer continuous Perceptron……Page 74
The class of functions realizable with a two-layer Perceptron……Page 78
The three-layer continuous Perceptron……Page 80
Application of a two-layer countinuous Perceptron to function indentification……Page 83
Application of a two-layer Perceptron to the mushroom classification problem……Page 84
Application of a two-layer Perceptron to the detection of the frequency of a sine wave……Page 85
Application of a multi-layer Perceptron to machine condition monitoring……Page 89
The learning speed of a continuous multi-layer Perceptron……Page 90
Initialization of weights and scaling the input and output……Page 91
Excercises……Page 92
Anthropomorphic pattern recognition with a self-organizing neural network……Page 93
The Bayes classification with a self-organinzing neural net algorithm……Page 98
Application of the self-organizing neural net algorithm to the classification of handwritten digits……Page 101
Topology preservation with a self-organizing algorithm……Page 103
Interpolation with self-organizing algorithm……Page 105
Master-slave and multi-net decomposition of the self-organizing neural net algorithm……Page 106
Application of the self-organizing algorithm to function identification……Page 107
Application of the self-organizing algorithm to robot arm control……Page 109
Application of the self-organizing algorithm to EEG signal analysis……Page 110
Application of the self-organizing algorithm to speech recognition……Page 112
Selecting and scaling of training vectors……Page 114
Some practical measures of performance of the self-organizing neural net algorithm……Page 115
Application of the self-organizing algorithm to signature identification……Page 118
Exercises……Page 119
Bibliography……Page 120
Index……Page 121