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By
Phiroz Bhagat, International Strategy Engines, USA
Description
- "Find it hard to extract and utilise valuable knowledge from the ever-increasing data deluge?" If so, this book will help, as
it explores pattern recognition technology and its concomitant role in extracting useful information to build technical and business
models to gain competitive industrial advantage.
- *Based on first-hand experience in the practice of pattern recognition technology
and its development and deployment for profitable application in Industry.
- Phiroz Bhagat is often referred to as the pioneer
of neural net and pattern recognition technology, and is uniquely qualified to write this book. He brings more than two decades of experience
in the "real-world" application of cutting-edge technology for competitive advantage in industry.
Two wave fronts are upon
us today: we are being bombarded by an enormous amount of data, and we are confronted by continually increasing technical and business
advances.
Ideally, the endless stream of data should be one of our major assets. However, this potential asset often tends to
overwhelm rather than enrich. Competitive advantage depends on our ability to extract and utilize nuggets of valuable knowledge and
insight from this data deluge. The challenges that need to be overcome include the under-utilization of available data due to competing
priorities, and the separate and somewhat disparate existing data systems that have difficulty interacting with each other.
Conventional
approaches to formulating models are becoming progressively more expensive in time and effort. To impart a competitive edge, engineering
science in the 21st century needs to augment traditional modelling processes by auto-classifying and self-organizing data; developing
models directly from operating experience, and then optimizing the results to provide effective strategies and operating decisions.
This approach has wide applicability; in areas ranging from manufacturing processes, product performance and scientific research, to
financial and business fields.
This monograph explores pattern recognition technology, and its concomitant role in extracting
useful knowledge to build technical and business models directly from data, and in optimizing the results derived from these models within
the context of delivering competitive industrial advantage. It is not intended to serve as a comprehensive reference source on the subject.
Rather, it is based on first-hand experience in the practice of this technology: its development and deployment for profitable application
in industry.
The technical topics covered in the monograph will focus on the triad of technological areas that constitute the
contemporary workhorses of successful industrial application of pattern recognition. These are: systems for self-organising data; data-driven
modelling; and genetic algorithms as robust optimizers.
Audience
This book is ideal for engineers, plant managers, business strategists, consultants, fund managers, financial analysts, R&D managers,
product formulators and developers directly involved with the process industry.
Contents
Preface
Acknowledgments
About the Author
Part I Philosophy
CHAPTER 1:
INTRODUCTION
CHAPTER 2:
PATTERNS WITHIN DATA
CHAPTER 3:
ADAPTING BIOLOGICAL
PRINCIPLES FOR DEPLOYMENT IN COMPUTATIONAL SCIENCE
CHAPTER 4:
ISSUES IN PREDICTIVE EMPIRICAL MODELING
Part
II Technology
CHAPTER 5:
SUPERVISED LEARNING?CORRELATIVE NEURAL NETS
CHAPTER 6:
UNSUPERVISED LEARNING: AUTO-CLUSTERING AND SELF-ORGANIZING DATA
CHAPTER 7:
CUSTOMIZING FOR INDUSTRIAL STRENGTH APPLICATIONS
CHAPTER 8:
CHARACTERIZING AND CLASSIFYING TEXTUAL MATERIAL
CHAPTER 9:
PATTERN RECOGNITION IN TIME
SERIES ANALYSIS
CHAPTER 10:
GENETIC ALGORITHMS
Part III Case Studies
CHAPTER
11:
HARNESSING THE TECHNOLOGY FOR PROFITABILITY
CHAPTER 12:
REACTOR MODELING THROUGH IN SITU ADAPTIVE LEARNING
CHAPTER 13:
PREDICTING PLANT STACK EMISSIONS TO MEET ENVIRONMENTAL LIMITS
CHAPTER 14:
PREDICTING
FOULING/COKING IN FIRED HEATERS
CHAPTER 15:
PREDICTING OPERATIONAL CREDITS
CHAPTER 16:
PILOT PLANT
SCALE-UP BY INTERPRETING TRACER DIAGNOSTICS
CHAPTER 17:
PREDICTING DISTILLATION TOWER TEMPERATURES: MINING DATA FOR
CAPTURING DISTINCT OPERATIONAL VARIABILITY
CHAPTER 18:
ENABLING NEW PROCESS DESIGN BASED ON LABORATORY DATA
CHAPTER
19:
FORECASTING PRICE CHANGES OF A COMPOSITE BASKET OF COMMODITIES
CHAPTER 20:
CORPORATE DEMOGRAPHIC TREND
ANALYSIS
EPILOGUE
Appendices
APPENDIX A1:
THERMODYNAMICS AND
INFORMATION THEORY
APPENDIX A2:
MODELING
| Bibliographic details |
Hardbound, 200 pages, publication date: MAR-2005
ISBN-13: 978-0-08-044538-0
ISBN-10: 0-08-044538-1
Imprint: ELSEVIER
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USD 113
EUR 88.95
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Last update: 22 Sep 2009
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