Applications of Neural NetworksAlan Murray Applications of Neural Networks gives a detailed description of 13 practical applications of neural networks, selected because the tasks performed by the neural networks are real and significant. The contributions are from leading researchers in neural networks and, as a whole, provide a balanced coverage across a range of application areas and algorithms. The book is divided into three sections. Section A is an introduction to neural networks for nonspecialists. Section B looks at examples of applications using `Supervised Training'. Section C presents a number of examples of `Unsupervised Training'. For neural network enthusiasts and interested, open-minded sceptics. The book leads the latter through the fundamentals into a convincing and varied series of neural success stories -- described carefully and honestly without over-claiming. Applications of Neural Networks is essential reading for all researchers and designers who are tasked with using neural networks in real life applications. |
Contents
Pattern Recognition and Classification | 35 |
Feature Location in the High Resolution Image | 53 |
Sex Recognition from Faces Using Neural Networks | 71 |
Diagnosis and Monitoring | 93 |
Dual Chamber Based Classification | 106 |
Classification of Cells in Cervical Smears | 113 |
Practical Implications of PAPNETAssisted Screening | 120 |
Multiphase flow monitoring in oil pipelines | 132 |
Inferential Estimation | 199 |
Concluding Remarks | 216 |
The Nested Network Method | 225 |
Simulation Results | 232 |
Signal Processing | 240 |
Bayesian Transversal Equaliser | 249 |
Decision Feedback Analyser | 257 |
Temporal Sequences Reinforcement Learning | 267 |
Generation of Datasets | 139 |
Effects of Noise on Inputs | 146 |
Discussion | 153 |
Prediction and Control | 157 |
Kohonens SelfOrganising Feature Maps | 163 |
Application of Multilayer Feedforward ANN | 177 |
On the Application of Artificial Neural Networks | 190 |
TDGammon 1 0 | 276 |
Conclusions | 282 |
The Application | 291 |
Discussion | 297 |
MixedMode Supervised and Unsupervised Training | 305 |
Supervised Learning | 313 |
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abnormal cells achieved adaptive adaptive equaliser algorithm approach approximation arrhythmia artificial neural network backpropagation Bayesian biomass carcinoma channel classifier clustering complex database day types decision boundary detect diagnosis equaliser error estimates example face feature points feed-forward network feedback filter gamma hidden layer hidden units hourly load pattern human ICDs IEEE implementation input pattern input space iterations K-Map learning samples linear load and valley load forecasting micro-feature multilayer perceptron neural net neurons noise nonlinear number of hidden optimal output node PAPNET PAPNET-assisted parameters patient pattern vector performance phase configuration phase fractions pixel position prediction problem procedure Prosopagnosia reinforcement learning resolution stage robot screening search region self-organising feature map SexNet signal smears Springer Science+Business Media supervised learning Table TD-Gammon technique temporal test set tiles training data training set valley load variables weights Wiener filter