- "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 operat
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
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
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