Pattern Recognition in Industry

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 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 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