Pattern Recognition in Industry (Hardcover)

Description:

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.

 

 

Table of Contents:

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 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

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

描述：
今天有兩個浪潮正向我們襲來：我們正面臨著大量的數據轟炸，以及不斷增加的技術和商業進步。

理想情況下，這無窮的數據流應該是我們的主要資產之一。然而，這個潛在的資產往往更容易壓倒我們，而不是豐富我們。競爭優勢取決於我們從這個數據洪流中提取和利用有價值的知識和洞察力的能力。需要克服的挑戰包括由於競爭優先順序而導致可用數據的低效利用，以及難以相互交互的現有數據系統之間的分離和有些不一致。

傳統的模型制定方法在時間和精力上變得越來越昂貴。為了提供競爭優勢，21世紀的工程科學需要通過自動分類和自組織數據，直接從運營經驗中開發模型，然後優化結果以提供有效的策略和運營決策。這種方法具有廣泛的應用性，涵蓋範圍從製造過程、產品性能和科學研究到金融和商業領域。

本專著探討了模式識別技術及其在從數據中直接建立技術和商業模型以及在優化這些模型的結果方面在提供競爭工業優勢的背景下提取有用知識的相關角色。它不旨在成為該主題的全面參考資料來源，而是基於對該技術在實踐中的第一手經驗：在工業應用中的開發和部署。

專著中涵蓋的技術主題將聚焦於構成當代成功工業應用模式識別的三個技術領域：自組織數據系統、數據驅動建模和遺傳算法作為強大的優化器。

目錄：
前言
致謝
關於作者
第一部分 哲學
第1章：引言
第2章：數據中的模式
第3章：將生物原理應用於計算科學中
第4章：預測性實證建模中的問題

第二部分 技術
第5章：監督學習-相關神經網絡
第6章：無監督學習：自動聚類和自組織數據
第7章：定制工業級應用
第8章：對文本材料進行特徵化和分類
第9章：時間序列分析中的模式識別
第10章：遺傳算法

第三部分 案例研究
第11章：利用技術實現盈利能力
第12章：通過原位自適應學習進行反應器建模
第13章：預測工廠煙囪排放以滿足環境限制
第14章：P