Applying Neural Networks: A Practical Guide

Front Cover
Morgan Kaufmann, 1996 - 303 pages
In this computer-based era, neural networks are an invaluable tool. They have been applied extensively in business forecasting, machine health monitoring, process control, and laboratory data analysis due to their modeling capabilities. There are numerous applications for neural networks, but a great deal of care and expertise is necessary to keep a neural-based project in working order.
This all-inclusive coverage gives you everything you need to put neural networks into practice. This informative book shows the reader how to plan, run, and benefit from a neural-based project without running into the roadblocks that often crop up. Theauthor uses the most popular type of neural network, the Multi-Layer Perceptron, and presents every step of its development. Each chapter presents a subsequent stage in network development through easy-to-follow discussion. Every decision and possible problem is considered in depth, and solutions are offered. The book includes a how-to-do-it reference section, and a set of worked examples. The second half of the book examines the sucessful application of neural networks in fields including signal processing, financial prediction, business decision support, and process monitoring and control. The book comes complete with a disk containing C and C++ programs to get you started.

Key Features
*Divides chapters into three sections for quick reference: Discussion, How to do it, and Examples
* Examines many case studies and real world examples to illustrate the methods presented
* Includes a disk with C and C++ programs which implement many of the techniques discussed in the text
* Allows the reader to devolop a neural network based solution

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Contents

Introduction
3
12 How does neural computing differ from traditional programming?
8
13 How are neural networks built?
10
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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