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Mathematical Modeling for Industrial Processes

  • Book
  • © 1970

Overview

Authors:
  1. Lassi Hyvärinen

    1. IBM-European Systems Research Institute, Geneva, Switzerland

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Part of the book series: Lecture Notes in Economics and Mathematical Systems (LNE, volume 19)

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Table of contents (7 chapters)

  1. Front Matter

    Pages N2-VI
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  2. Basic Concepts

    • Lassi Hyvärinen
    Pages 1-10

  3. Optimizing Models

    • Lassi Hyvärinen
    Pages 11-27

  4. Methods of Optimum Search

    • Lassi Hyvärinen
    Pages 28-46

  5. Design of Experiments

    • Lassi Hyvärinen
    Pages 47-68

  6. Dynamic Covariance Analysis

    • Lassi Hyvärinen
    Pages 69-81

  7. Principal Component Analysis

    • Lassi Hyvärinen
    Pages 82-104

  8. Regression Analysis

    • Lassi Hyvärinen
    Pages 105-120

  9. Back Matter

    Pages 121-125
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Keywords

About this book

These notes are based on the material presented in a series of lec­ tures in the IBM Systems Research Institute (ESRI) in Geneva durJng 1967-1969 to systems engineers working in the design and programming of computer systems for control and monitoring of i~nustrial proc­ esses. The purpose of the lectures and this book is to give a survey of dif­ ferent approaches in developing models to describe the behavior of the process in terms of controllable variables. It does not cover the theory of control, stability of control systems, nor techniques in data acquisition or problems in instrumentation and sampling. But certain aspects in the organization of data collection and design of experiments are obtained as side products, notably the concept of orthogonality. The reader is assumed to have a working knowledge of elementary prob­ ability theory and mathematical statistics. Therefore, the text con­ tains no introduction to these concepts. The author is aware of some inaccuracies in not making proper dis­ tinction between population parameters and their sample estimates in the text, but this should alw~s be evident from the context. The same applies to the occasional replacement of number of degrees of freedom by the number of samples in the data. In practice, computer collected sets of data consist of a high number of samples and the difference between the two is inSignificant.

Authors and Affiliations

  • IBM-European Systems Research Institute, Geneva, Switzerland

    Lassi Hyvärinen

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