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Data-Driven Prediction for Industrial Processes and Their Applications

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  • © 2018

Overview

Authors:
  1. Jun Zhao

    1. Dalian University of Technology, Dalian, China

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  2. Wei Wang

    1. Dalian University of Technology, Dalian, China

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  3. Chunyang Sheng

    1. Shandong University of Science and Technology, Qingdao, China

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  • Features data-driven modeling algorithms for different industrial prediction requirements
  • Discusses multi-scale (short, median, long) prediction, multi-type prediction (time series and factor-based), and interval-based prediction
  • Includes case studies based on real-world industrial predictions

Part of the book series: Information Fusion and Data Science (IFDS)

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Table of contents (9 chapters)

  1. Front Matter

    Pages i-xvi
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  2. Introduction

    • Jun Zhao, Wei Wang, Chunyang Sheng
    Pages 1-11

  3. Data Preprocessing Techniques

    • Jun Zhao, Wei Wang, Chunyang Sheng
    Pages 13-52

  4. Industrial Time Series Prediction

    • Jun Zhao, Wei Wang, Chunyang Sheng
    Pages 53-119

  5. Factor-Based Industrial Process Prediction

    • Jun Zhao, Wei Wang, Chunyang Sheng
    Pages 121-157

  6. Industrial Prediction Intervals with Data Uncertainty

    • Jun Zhao, Wei Wang, Chunyang Sheng
    Pages 159-222

  7. Granular Computing-Based Long-Term Prediction Intervals

    • Jun Zhao, Wei Wang, Chunyang Sheng
    Pages 223-267

  8. Parameter Estimation and Optimization

    • Jun Zhao, Wei Wang, Chunyang Sheng
    Pages 269-350

  9. Parallel Computing Considerations

    • Jun Zhao, Wei Wang, Chunyang Sheng
    Pages 351-383

  10. Data-Based Prediction for Energy Scheduling of Steel Industry

    • Jun Zhao, Wei Wang, Chunyang Sheng
    Pages 385-436

  11. Back Matter

    Pages 437-443
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Keywords

About this book

This book presents modeling methods and algorithms for data-driven prediction and forecasting of practical industrial process by employing machine learning and statistics methodologies. Related case studies, especially on energy systems in the steel industry are also addressed and analyzed. The case studies in this volume are entirely rooted in both classical data-driven prediction problems and industrial practice requirements. Detailed figures and tables demonstrate the effectiveness and generalization of the methods addressed, and the classifications of the addressed prediction problems come from practical industrial demands, rather than from academic categories. As such, readers will learn the corresponding approaches for resolving their industrial technical problems. Although the contents of this book and its case studies come from the steel industry, these techniques can be also used for other process industries. This book appeals to students, researchers, and professionals withinthe machine learning and data analysis and mining communities.

Authors and Affiliations

  • Dalian University of Technology, Dalian, China

    Jun Zhao, Wei Wang

  • Shandong University of Science and Technology, Qingdao, China

    Chunyang Sheng

About the authors

Jun Zhao is currently a Professor with the School of Control Science and Engineering, Dalian University of Technology, China.

Chunyang Sheng is currently a lecturer with the School of Electrical Engineering and Automation, Shandong University of Science and Technology, China. 

Wei Wang is currently a Professor with the School of Control Science and Engineering, Dalian University of Technology, China.

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