Pattern recognition in industry
- "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 in...
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| Médium: | E-kniha Kniha |
| Jazyk: | angličtina |
| Vydáno: |
Oxford
Elsevier
2005
Elsevier Science & Technology Elsevier Science |
| Vydání: | 1 |
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| ISBN: | 9780080445380, 0080445381 |
| On-line přístup: | Získat plný text |
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| Abstract | - "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. |
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| AbstractList | - "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. "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. "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. |
| Author | Bhagat, Phiroz |
| Author_xml | – sequence: 1 fullname: Bhagat, Phiroz |
| BackLink | https://cir.nii.ac.jp/crid/1130000793893543936$$DView record in CiNii |
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| Notes | Includes bibliographical references and index |
| OCLC | 162130068 |
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| PageCount | 201 |
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| PublicationDate | 2005 2005-03-30 |
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| PublicationPlace | Oxford |
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| PublicationYear | 2005 |
| Publisher | Elsevier Elsevier Science & Technology Elsevier Science |
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| Snippet | - "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... "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... |
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| SubjectTerms | Digital techniques Image processing Image processing -- Digital techniques Pattern perception Pattern recognition systems |
| TableOfContents | Front Cover -- Pattern Recognition in Industry -- Copyright Page -- Contents -- Preface -- Acknowledgments -- About the Author -- Part I: Philosophy -- Chapter 1. Introduction -- 1.1. Distinguishing Knowledge and Information from Data -- 1.2. Whence Pattern Recognition Technology -- 1.3. Thermodynamic Concept of Order Leading to Information Theory -- 1.4. Modeling Informed by Observation -- 1.5. Pattern Recognition Technology Triad -- References -- Chapter 2. Patterns Within Data -- 2.1. Types of Data -- 2.2. Characterizing Data -- 2.3. Distance Between Data -- 2.4. Organizing Data-Clustering / Auto-Classification -- 2.5. Organizing Data-Data Series Resonance -- 2.6. Organizing Data-Correlative Modeling -- References -- Chapter 3. Adapting Biological Principles for Deployment in Computational Science -- 3.1. Learning Organisms-An Introduction to Neural Nets -- 3.2. Supervised Learning -- 3.3. Unsupervised Learning -- 3.4. Models that Self-Organize Data (Unsupervised Learning) as well as Correlate them with Dependent Outcomes (Supervised Learning) -- 3.5. Genetic Algorithms -- References -- Chapter 4. Issues in Predictive Empirical Modeling -- 4.1. Pre-Conditioning Data: Pre- and Post-Processing -- 4.2. Detecting Extrapolative Conditions -- 4.3. Embedding Mechanistic Understanding / Experiential Judgment to Enhance Extrapolative Robustness -- 4.4. Insight into Model Behavior -- Part II: Technology -- Chapter 5. Supervised Learning-Correlative Neural Nets -- 5.1. Supervised Learning with Back-Propagation Neural Nets -- 5.2. Feedforward-Exercising the BP Net in Predictive Mode-Neuron Transformation Function -- 5.3. BP Training-Connection Weights Adjusted by the "Delta Rule" to Minimize Learning Errors -- 5.4. Back-Propagation Equations for General Transformation Functions -- 5.5. Back-Propagation Equations for Sigmoidal Transformation Functions 5.6. Conjugate Gradient Methodology for Rapid and Robust Convergence -- 5.7. Separating Signal from Noise in Training -- 5.8. Pre-Conditioning Data for BP Nets -- 5.9. Supervised Learning with Radial Basis Function Neural Nets -- 5.10. Seeding the Input Data Space with RBF Cluster Centers -- 5.11. Assigning Spheres of Influence to each Cluster -- 5.12. Activating Clusters from a Point in the Data Space -- 5.13. Developing RBF Correlation Models-Assigning Weights to Map Outcome -- 5.14. Pre-Conditioning Data for RBF Nets -- 5.15. Neural Net Correlation Models -- References -- Chapter 6. Unsupervised Learning: Auto-Clustering and Self-Organizing Data -- 6.1. Unsupervised Learning-Value to Industry -- 6.2. Auto-Clustering Using Radial Basis Functions -- 6.3. RBF Cluster Radius -- 6.4. Competitive Learning -- 6.5. Data Pre-Conditioning for Competitive Learning -- References -- Chapter 7. Customizing for Industrial Strength Applications -- 7.1. Modeling: The Quest for Explaining and Predicting Processes -- 7.2. Combining Empiricism with Mechanistic Understanding -- 7.3. Embedding an Idealized (Partially Correct) Model -- 7.4. Embedding A Priori Understanding in the Form of Constraints -- 7.5. Incorporating Mixed Data Types -- 7.6. Confidence Measure for Characterizing Predictions -- 7.7. Interpreting Trained Neural Net Structures -- 7.8. Graphical Interpretation of Trained Neural Net Structures -- References -- Chapter 8. Characterizing and Classifying Textual Material -- 8.1. Capturing a Document's Essential Features through Fingerprinting -- 8.2. Similar Documents Auto-Classified into Distinct Clusters -- 8.3. Activity Profiles of Authors Provide Competitive Insight -- 8.4. Visualizing a Document's Contents -- 8.5. Identifying Keywords through Entropic Analysis of Text Documents 16.3. Genetic Algorithm-Simulation Model Coupling -- 16.4. Results and Conclusion -- Chapter 17. Predicting Distillation Tower Temperatures: Mining Data for Capturing Distinct Operational Variability -- 17.1. Background -- 17.2. Issue -- 17.3. Model Configuration -- 17.4. Identifying Distinctly Different Operating Conditions -- 17.5. Results -- Chapter 18. Enabling New Process Design Based on Laboratory Data -- 18.1. Background -- 18.2. Model Configuration-Bi-Level Focus for "Spot-Lighting" Region of Interest -- 18.3. Model Results -- 18.4. Conclusion -- Chapter 19. Forecasting Price Changes of a Composite Basket of Commodities -- 19.1. Background -- 19.2. Approach and Model Configuration -- 19.3. Model Results -- 19.4. Conclusions -- Chapter 20. Corporate Demographic Trend Analysis -- 20.1. Background -- 20.2. Issues -- 20.3. Approach and Model Configuration -- 20.4. Model Results and Conclusions -- Epilogue -- Appendices -- Appendix A1. Thermodynamics and Information Theory -- A1.1. Thermodynamic Concepts Set the Stage for Quantifying Information -- A1.2. Equilibrium as a State of Disorder-Organization as a Value-Adding Process -- A1.3. Entropy, Disorder, and Uncertainty -- A1.4. Opportunities Found in Imbalances -- A1.5. Appreciation through Quantification -- A1.6. Quantifying Information Transfer -- A1.7. Information Content in a System -- References -- Appendix A2. Modeling -- A2.1. What Are Models -- A2.2. Mechanistic Modeling-General Laws -- A2.3. Particular Laws and Constitutive Relations -- A2.4. Combining General Laws and Constitutive Relations -- A2.5. Modeling Directly from Data -- Reference -- Index 8.6. Automation Shrinks Time and Resources Required to Keep up with the World -- References -- Chapter 9. Pattern Recognition in Time Series Analysis -- 9.1. Leading Indices as Drivers -- 9.2. Concept of Resonance in Quantifying Similarities between Time Series -- 9.3. Identifying Leading Indicators -- 9.4. Forecasting -- Reference -- Chapter 10. Genetic Algorithms -- 10.1. Background -- 10.2. Definitions -- 10.3. Setting the Stage -- 10.4. Selection -- 10.5. Mating -- 10.6. Mutation -- 10.7. "Breeding" Fit Solutions -- 10.8. Discovering Profitable Operating Strategies -- 10.9. Product Formulation -- References -- Part III: Case Studies -- Chapter 11. Harnessing the Technology for Profitability -- 11.1. Process Industry Application Modes -- 11.2. Business Applications -- 11.3. Case Studies that Follow -- Chapter 12. Reactor Modeling Through in Situ Adaptive Learning -- 12.1. Background -- 12.2. Reactor Catalyst Deactivation -- 12.3. Model Configuration -- 12.4. In Situ Modeling Scheme -- 12.5. Validation Procedure -- 12.6. Validation Results -- 12.7. Roles Played by Modeling and Plant Operational Teams -- 12.8. Competitive Advantage Derived through this Approach -- Reference -- Chapter 13. Predicting Plant Stack Emissions to Meet Environmental Limits -- 13.1. Background -- 13.2. Reactor Flow and Model Configuration -- 13.3. Model Training and Results -- 13.4. Identifying Optimal Operating Windows for Enhancing Profits -- Chapter 14. Predicting Fouling/Coking in Fired Heaters -- 14.1. Background -- 14.2. Model Configuration -- 14.3. Model Results -- 14.4. Conclusions -- Chapter 15. Predicting Operational Credits -- 15.1. Background -- 15.2. Issues -- 15.3. Model Configuration -- 15.4. Model Results -- 15.5. Plant Follow-Up -- Chapter 16. Pilot Plant Scale-up by Interpreting Tracer Diagnostics -- 16.1. Background -- 16.2. Issue |
| Title | Pattern recognition in industry |
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