Qualitative Process Theory Using Linguistic Variables

Paperback (Softcover reprint of the original 1st ed. 1989)
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Editorial Reviews

Booknews
An exploration of the use of linguistic variables as a semiquantitative extension to the qualitative value and relationship representations in qualitative process theory, for application in fuzzy logic control. Acidic paper. Annotation c. Book News, Inc., Portland, OR booknews.com
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Product Details

  • ISBN-13: 9781461396734
  • Publisher: Springer New York
  • Publication date: 1/19/2012
  • Series: Symbolic Computation / Artificial Intelligence Series
  • Edition description: Softcover reprint of the original 1st ed. 1989
  • Edition number: 1
  • Pages: 156
  • Product dimensions: 6.14 (w) x 9.21 (h) x 0.37 (d)

Table of Contents

1 Overview.- 2 Fuzzy Logic Control.- 2.1 Classical Control Theory.- 2.2 A New Approach to Control of Complex Systems.- 2.3 Fuzzy Control.- 2.3.1 Fuzzification of Input.- 2.3.2 Rule Interpretation.- 2.3.3 Defuzzification.- 2.4 Extensions of the Fuzzy Control Paradigm.- 2.5 The Next Step: Fuzzy-Model-Based Control.- 3 Introduction to Qualitative Process Theory.- 3.1 Uses of QP Theory.- 3.2 Process versus Device-Centered Theories.- 3.3 Qualitative Process Theory - Definitions and Examples.- 3.4 Reasoning in QP Theory.- 3.5 Historical Background.- 3.6 Summary.- 4 Application of QP Theory to Process Control - An Example.- 4.1 The Reaction Process.- 4.2 Countercurrent Heat Flow.- 4.3 Basic Deductions.- 5 Ambiguity in QP Theory.- 5.1 Ambiguity in QP Theory.- 5.2 Representation Alternatives.- 5.2.1 Representations for Uncertainty.- 5.2.2 Representations for Parameters of Physical Systems.- 5.2.3 Characterizations of Functional Relationships.- 5.3 Related Work.- 5.4 Linguistic Extensions to QP Descriptions.- 6 Linguistic Variables.- 6.1 Introduction to Linguistic Variables.- 6.2 Approximate Reasoning and the Compositional Rule of Inference.- 6.3 Similarity of Linguistic Variables and Relations.- 6.4 Support-Pair Certainty.- 6.4.1 Support Logic Programming.- 6.4.2 Support-Pair Certainty and Fuzzy Sets.- 6.4.3 Relationship of SLP with Linguistic Variables and the Compositional Rule of Inference.- 6.5 Truth Maintenance with Numeric Certainty Estimates.- 6.5.1 Review of Methods for Uncertainty Management in AI.- 6.5.2 Assumption-Based Truth Maintenance.- 6.5.3 SLP and Assumption-Based Truth Maintenance.- 6.5.4 Symbolic Computation of Belief Functions.- 6.5.5 Example.- 6.5.6 Discussion and Related Research.- 6.6 Summary.- 7 Linguistic Quantity Spaces.- 7.1 A Functional Overview.- 7.2 Computing the Consequences of a Linguistic Quantity Space.- 7.2.1 Representation of Parameter Values and Relations.- 7.2.2 Quantity Space Deductions.- 7.2.3 Implementing the Quantity Space Deductions.- 7.3 Linguistic Measurement Interpretation.- 8 Characterization of Functional Relationships.- 8.1 Sources of Ambiguity in QP Theory Models.- 8.1.1 Ambiguity 1 - Offgas Exit Temperature.- 8.1.2 Ambiguity 2 - Product Temperature.- 8.1.3 Ambiguity 3 - Material-Flow Rates.- 8.1.4 Discussion.- 8.2 Fuzzy Relational Algorithms.- 8.2.1 Computing Individual Influence Effects.- 8.2.2 Adjusting for Description Applicability.- 8.2.3 Combining Influence Effects.- 8.3 Annotation Management.- 8.4 Examples.- 8.5 Summary.- 9 Qualitative Perturbation Analysis.- 9.1 Qualitative Perturbation Analysis.- 9.2 Extended Perturbation Analysis.- 9.3 Linguistic Perturbation Analysis.- 9.4 LPA Extended Example.- 9.4.1 Input Script.- 9.4.2 Output Trace.- 9.5 Summary.- 10 Evaluation and Conclusion.- 10.1 Review.- 10.2 Evaluation.- 10.3 Further Research.- 10.4 Conclusion.- 10.5 References.
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