Qualitative Process Theory Using Linguistic Variables

Bruce D'Ambrosio

ISBN 10: 1461396735 / ISBN 13: 9781461396734

Editore: Springer New York, Springer New York, 2012

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Druck auf Anfrage Neuware - Printed after ordering - 8. 5 Summary In this chapter we have identified three basic patterns of influences that lead to ambiguity in the QP analysis of the basic active furnace state. We have then shown how modification of these patterns, by adding equilibrium values and sensitivity annotations on influence arcs, could permit resolu tion of the ambiguities. Finally, we have described in detail the extensions needed to the basic influence resolution algorithm in QP theory to oper ate on these extended descriptions. We have also shown that the modified influence resolution algorithm corrects an error in Forbus' original method for combining influences. We have then presented an extended example in which introduction of equilibrium assumptions eliminates all ambigu ity in the influence resolution deduction. In the next chapter we extend these techniques further, by developing a qualitative perturbation analysis technique that permits us to answer 'what ir' control questions; then we extend this technique to obtain quantitative, as well as qualitative, effects of hypothetical control actions. 8.

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Dati bibliografici

• Titolo: Qualitative Process Theory Using Linguistic Variables
• Editore: Springer New York, Springer New York
• Data di pubblicazione: 2012
• Lingua: Inglese
• ISBN 10: 1461396735
• ISBN 13: 9781461396734
• Rilegatura: Taschenbuch
• Condizione: Neu

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Riassunto

8. 5 Summary In this chapter we have identified three basic patterns of influences that lead to ambiguity in the QP analysis of the basic active furnace state. We have then shown how modification of these patterns, by adding equilibrium values and sensitivity annotations on influence arcs, could permit resolu­ tion of the ambiguities. Finally, we have described in detail the extensions needed to the basic influence resolution algorithm in QP theory to oper­ ate on these extended descriptions. We have also shown that the modified influence resolution algorithm corrects an error in Forbus' original method for combining influences. We have then presented an extended example in which introduction of equilibrium assumptions eliminates all ambigu­ ity in the influence resolution deduction. In the next chapter we extend these techniques further, by developing a qualitative perturbation analysis technique that permits us to answer "what ir' control questions; then we extend this technique to obtain quantitative, as well as qualitative, effects of hypothetical control actions. 8.

Contenuti

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.