Pattern Recognition in IndustryBy
- Phiroz Bhagat, International Strategy Engines, USA
- "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.
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.
Hardbound, 200 Pages
Published: March 2005
- Preface Acknowledgments About the Author Part I Philosophy
CHAPTER 1: INTRODUCTIONCHAPTER 2: PATTERNS WITHIN DATACHAPTER 3: ADAPTING BIOLOGICAL PRINCIPLES FOR DEPLOYMENT IN COMPUTATIONAL SCIENCECHAPTER 4: ISSUES IN PREDICTIVE EMPIRICAL MODELING
Part II Technology
CHAPTER 5: SUPERVISED LEARNINGCORRELATIVE NEURAL NETSCHAPTER 6: UNSUPERVISED LEARNING: AUTO-CLUSTERING AND SELF-ORGANIZING DATACHAPTER 7: CUSTOMIZING FOR INDUSTRIAL STRENGTH APPLICATIONSCHAPTER 8: CHARACTERIZING AND CLASSIFYING TEXTUAL MATERIALCHAPTER 9: PATTERN RECOGNITION IN TIME SERIES ANALYSISCHAPTER 10: GENETIC ALGORITHMS
Part III Case Studies
CHAPTER 11: HARNESSING THE TECHNOLOGY FOR PROFITABILITYCHAPTER 12: REACTOR MODELING THROUGH IN SITU ADAPTIVE LEARNINGCHAPTER 13: PREDICTING PLANT STACK EMISSIONS TO MEET ENVIRONMENTAL LIMITSCHAPTER 14: PREDICTING FOULING/COKING IN FIRED HEATERSCHAPTER 15: PREDICTING OPERATIONAL CREDITSCHAPTER 16: PILOT PLANT SCALE-UP BY INTERPRETING TRACER DIAGNOSTICSCHAPTER 17: PREDICTING DISTILLATION TOWER TEMPERATURES: MINING DATA FOR CAPTURING DISTINCT OPERATIONAL VARIABILITYCHAPTER 18: ENABLING NEW PROCESS DESIGN BASED ON LABORATORY DATACHAPTER 19: FORECASTING PRICE CHANGES OF A COMPOSITE BASKET OF COMMODITIESCHAPTER 20: CORPORATE DEMOGRAPHIC TREND ANALYSISEPILOGUE
Appendices APPENDIX A1: THERMODYNAMICS AND INFORMATION THEORYAPPENDIX A2: MODELING