| Edition |
1st ed. |
| Description |
1 online resource (xix, 180 pages) : illustrations (some color) |
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text txt rdacontent |
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computer c rdamedia |
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online resource cr rdacarrier |
| Contents |
Preface -- -- Acknowledgments -- About the Author -- <CENTER>Part I Philosophy</CENTER> -- -- 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 -- <CENTER>Part II Technology</CENTER> -- CHAPTER 5: SUPERVISED LEARNINGCORRELATIVE 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 -- <CENTER>Part III Case Studies</CENTER> -- 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 -- <CENTER>Appendices</CENTER> -- APPENDIX A1: THERMODYNAMICS AND INFORMATION THEORY -- APPENDIX A2: MODELING. |
| Bibliography |
Includes bibliographical references and index. |
| Note |
Print version record. |
| Summary |
"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.</li> <li>*Based on first-hand experience in the practice of pattern recognition technology and its development and deployment for profitable application in Industry.</li> <li>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.</li></ul> 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. <li>"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. <li>Based on first-hand experience in the practice of pattern recognition technology and its development and deployment for profitable application in Industry. <li>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 |
| Subject |
Image processing -- Digital techniques.
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Pattern recognition systems.
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Pattern Recognition, Automated |
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Traitement d'images -- Techniques numériques.
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Reconnaissance des formes (Informatique)
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digital imaging.
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COMPUTERS -- Optical Data Processing.
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Image processing -- Digital techniques
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Pattern recognition systems
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| Other Form: |
Print version: Bhagat, Phiroz. Pattern recognition in industry. 1st ed. Amsterdam ; Boston ; London : Elsevier, 2005 0080445381 9780080445380 (DLC) 2004063257 (OCoLC)57373672 |
| ISBN |
9780080445380 |
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0080445381 |
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0080456022 (electronic bk.) |
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9780080456027 (electronic bk.) |
| Standard No. |
AU@ 000051558364 |
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AU@ 000051860681 |
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AU@ 000060527177 |
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CHBIS 005659209 |
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CHNEW 001005529 |
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CHVBK 167134272 |
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DEBBG BV042315665 |
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DEBBG BV043043867 |
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DEBBG BV044084376 |
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DEBSZ 36775567X |
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DEBSZ 422262366 |
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NZ1 12434386 |
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