All the three techniques mentioned above may make use of fuzzy sets and fuzzy logic (for fuzzy classification, fuzzy rules or fuzzy matching) but this does not effect the discussion of the applicability to NDT problems in the next section. [Pg.99]

To know about fuzzy sets and fuzzy logic [Pg.439]

Fuzzy logic and fuzzy set theory are applied to various problems in chemistry. The applications range from component identification and spectral Hbrary search to fuzzy pattern recognition or calibrations of analytical methods. [Pg.466]

An overview over different applications of fuzzy set theory and fuzzy logic is given in [15] (see also Chapter IX, Section 1.5 in the Handbook). [Pg.466]

Here, the application of fuzzy logic for multicomponent spectral analysis is described. [Pg.466]

Fuzzy logic extends the Boolean logic so as to handle information about truth values which are between absolutely true and "absolutely false . [Pg.481]

A series of monographs and correlation tables exist for the interpretation of vibrational spectra [52-55]. However, the relationship of frequency characteristics and structural features is rather complicated and the number of known correlations between IR spectra and structures is very large. In many cases, it is almost impossible to analyze a molecular structure without the aid of computational techniques. Existing approaches are mainly based on the interpretation of vibrational spectra by mathematical models, rule sets, and decision trees or fuzzy logic approaches. [Pg.529]

Fragment Reduced to an Environment that is Limited (FREL) 516 Frequency 215 Fr ejacque number 55 Friedel-Crafts alkylation 193 Frontier Molecular Orital (FMO) theory 179 Functional group 188, 192, 403 Fuzzy logic 465, 479 Fuzzy set 465 [Pg.639]

One variation of rule-based systems are fuzzy logic systems. These programs use statistical decision-making processes in which they can account for the fact that a specific piece of data has a certain chance of indicating a particular result. All these probabilities are combined in order predict a final answer. [Pg.109]

While the single-loop PID controller is satisfactoiy in many process apphcations, it does not perform well for processes with slow dynamics, time delays, frequent disturbances, or multivariable interactions. We discuss several advanced control methods hereafter that can be implemented via computer control, namely feedforward control, cascade control, time-delay compensation, selective and override control, adaptive control, fuzzy logic control, and statistical process control. [Pg.730]

Fuzzy Logic Control The apphcation of fuzzy logic to process control requires the concepts of Fuzzy rules and fuzzy inference. A fuzzy rule, also known as a fuzzy IF-THEN statement, has the form [Pg.735]

In addition to single-loop process controllers, products that have benefited from the implementation of fuzzy logic are [Pg.735]

Sometimes fuzzy logic controllers are combined with pattern recognition software such as artificial neural networks (Kosko, Neural Networks and Fuzzy Systems, Prentice Hall, Englewood Cliffs, New Jersey, 1992). [Pg.735]

Fuzzy logic control systems 10.2.1 Fuzzy set theory [Pg.326]

An important aspect of fuzzy logic is the ability to relate sets with different universes of discourse. Consider the relationship [Pg.330]

MATLAB Fuzzy Inference System (FIS) editor can be found in Appendix 1. Figure 10.16 shows the control surface for the 11 set rulebase fuzzy logic controller. [Pg.344]

Self-Organizing Fuzzy Logic Control (SOFLC) is an optimization strategy to create and modify the control rulebase for a FLC as a result of observed system performance. The SOFLC is particularly useful when the plant is subject to time-varying parameter changes and unknown disturbances. [Pg.344]

Fig. 10.16 Control surface for 11 set rulebase fuzzy logic controller. |

Fig. 10.17 Self-Organizing Fuzzy Logic Control system. |

Fig. A1.8 Simulink implementation of inverted pendulum fuzzy logic control problem. |

Yan, J., Ryan, M. and Power, J. (1994) Using fuzzy logic - Towards intelligent systems, Prentice-Hall International (UK), Hemel Hempstead, UK. [Pg.432]

See also in sourсe #XX -- [ Pg.109 ]

See also in sourсe #XX -- [ Pg.0 , Pg.3 , Pg.326 ]

See also in sourсe #XX -- [ Pg.688 , Pg.691 ]

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