| Description |
xix, 180 pages : illustrations ; 25 cm |
| Contents |
Ch. 1. Introduction -- Ch. 2. Patterns within data -- Ch. 3. Adapting biological principles for deployment in computational science -- Ch. 4. Issues in predictive empirical modeling -- Ch. 5. Supervised learning - correlative neural nets -- Ch. 6. Unsupervised learning : auto-clustering and self-organizing data -- Ch. 7. Customizing for industrial strength applications -- Ch. 8. Characterizing and classifying textual material -- Ch. 9. Pattern recognition in time series analysis -- Ch. 10. Genetic algorithms -- Ch. 11. Harnessing the technology for profitability -- Ch. 12. Reactor modeling through in situ adaptive learning -- Ch. 13. Predicting plant stack emissions to meet environmental limits -- Ch. 14. Predicting fouling/coking in fired heaters -- Ch. 15. Predicting operational credits -- Ch. 16. Pilot plant scale-up by interpreting tracer diagnostics -- Ch. 17. Predicting distillation tower temperatures : mining data for capturing distinct operational variability |
|
Ch. 18. Enabling new process design based on laboratory data -- Ch. 19. Forecasting price changes of a composite basket of commodities -- Ch. 20. Corporate demographic trend analysis -- App. A1. Thermodynamics and information theory -- App. A2. Modeling |
| Bibliography |
Includes bibliographical references and index |
| Subject |
Image processing -- Digital techniques.
|
|
Pattern recognition systems.
|
| LC no. |
2004063257 |
| ISBN |
0080445381 |
|
0080445381 (cased) |
|
9780080445380 |
|
