Applying Neural Networks: A Practical Guide

Front Cover
Morgan Kaufmann, 1996 - 303 pages
"Only a few years ago the neural networks were touted as the solution to all our worries. Artificial Intelligence the easy way! Of course nothing is ever that easy and it was not long before people realised that a lot of care and expertise are required to prevent a neural-based project going irretrievably wrong. This book presents the practical solutions required to successfully apply neural networks. Neural networks have become an invaluable tool to both academia and industry. Whether they are applied to business forecasting, machine health monitoring, process control or laboratory data analysis, researchers and managers alike are discovering the modelling power of neural networks. This book is not another selection of papers, but a fully integrated, structured approach the study and application of neural networks. It will be of interest to the industrial reader who wishes to benefit from the advantages neural networks provide and to the student or academic researcher who, rather than wishing to study neural networks for their own sake, wishes to learn how to use them. This book is Neural Networks Made Easy, showing you how to plan, run and succeed with a neural network based project. By taking the most popular type of neural network - the Multi Layer Perceptron - and presenting every step in its development, every decision to be made and every problem to overcome (plus solution!), it guides the reader to a successful project conclusion. Each chapter presents a stage in network development with an easy to follow discussion, a how-to-do-it reference section and a set of worked examples. The second half of the book is devoted to an in-depth study of a number of successful neural network applications in fields such as signal processing, financial predication, business decision support, and process monitoring and control. The book also comes complete with a disk of C and C++ programs to help you on your way. Everything you need, in fact, to put neural networks in practice"--Page 4 of cover.

From inside the book

Contents

Introduction
11
12 How does neural computing differ from traditional programming?
11
13 How are neural networks built?
11
14 How do neural networks learn?
11
15 What do I need to build an MLP?
14
17 The generalisationaccuracy tradeoff
15
18 Implementation details
17
19 Activation and learning equations
19
69 Autoassociative network novelty detection
160
Network use and analysis
165
73 Traversing a network
175
74 Summary
176
75 Calculating the derivatives
177
a worked example
179
Managing a neural network based project
183
82 Development platform
186

Modelling a pendulum
20
Data encoding and recoding
23
22 Data type classification
25
23 Initial statistical calculations
26
24 Dimensionality reduction
27
25 Scaling a data set
31
26 Neural encoding methods
34
27 Temporal data
43
28 When to carry out recoding
46
29 Implementation details
47
Building a network
51
33 Training neural networks
65
34 Implementation details
70
Time varying systems
77
42 Neural networks for predicting or classifying time series
82
43 Choosing the best method for the task
84
44 Predicting more than one step into the future
90
45 Learning separate paths through state space
91
46 Recurrent networks as models of finite state automata
98
47 Summary of temporal neural networks
103
Data collection and validation
105
52 Building the training and test sets
108
53 Data quality
113
54 Calculating entropy values for a data set
126
55 Using a forwardinverse model to solve ill posed problems
128
Output and error analysis
133
62 What do the errors mean?
134
63 Error bars and confidence limits
135
64 Methods for visualising errors
143
65 Novelty detection
145
66 Implementation details
148
67 A simple two class example
154
A mail shot targeting example
155
83 Project personnel
187
84 Project costs
188
85 The benefits of neural computing
189
87 Alternatives to a neural computing approach
190
89 Project documentation
193
810 System maintenance
194
Review of neural applications
195
Introduction to Part II
197
Neural networks and signal processing
199
93 Preprocessing techniques for visual processing
202
94 Neural filters in the Fourier and temporal domains
206
95 Speech recognition
210
96 Production quality control
214
97 An artistic style classifier
217
98 Fingerprint analysis
220
Financial and Business Modelling
221
103 Financial time series prediction
223
104 Review of published findings
226
105 Conclusion
235
Industrial process modelling
237
predicting driver alertness
245
114 Training the neural networks
251
115 Robot control by reinforcement learning
255
116 Summary
261
Conclusions
263
Using the accompanying software
267
132 Neural network code
268
133 Data preparation routines
274
Glossary
281
Bibliography
291
Index
301
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