Fuzzy logic applications

Yager RR, Zadeh LA (eds) (1992), An introduction to fuzzy logic applications in intelligent systems. Kluwer, Boston Dordrecht London, esp chap 1, p 1... 

S, T. Welstead, Neural Networks and Fuzzy Logic Applications in C/C++, Wiley, New York, 1994. 

C von Altrock, B Krause. Fuzzy logic application note optimization of a water treatment system, http //www.fuzzytech.eom/e.a.dek.htm... 

Bojadziev, G. Bojadziev, M. 1995. Fuzzy sets, fuzzy logic, applications. World Scientific Publishing Co Pte Ltd. Singapore. 

Although, the basic algorithms of fuzzy logic have been around 45 years however, the last two decades have been witnessed its more and more application in the variety of textile problems. The fuzzy logic was first applied to the textile problem by Pan et al. in 1988 [2]. However, the publications on fuzzy logic application in textiles have become more pronoimced after 2000. This chapter presents an overview of the application of fuzzy logic in various field of textile engineering. 

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. 

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. 

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). 

Here, the application of fuzzy logic for multicomponent spectral analysis is described. 

An early application of fuzzy logic was in the control of a cement kiln and applications to similar or identical systems have continued to appear (see, for example, Jarvensivu et al.1). This is an example of a practical application in which generation of high quality product may depend on the skill and experience of a small number of workers. Even when computer control is available, workers may describe the way that they adjust conditions to provide a high-quality product by statements such as, "The kiln rotation rate should be lowered slightly," and that, in order to compensate, "The temperature should be diminished just a bit." Fuzzy logic now has a lengthening track record of use in such situations. 

Control problems represent a major area of application for fuzzy logic since reliable process control may rely on the long-term expertise of one or a few people, and those people may be able to frame their knowledge of the system only in imprecise terms. A typical example is the control of pH in a crystallization reactor.2 A similar application was described by Puig and co-... 

Fuzzy logic is often presented as an extension in books that cover expert systems. Few texts exist in which the applications of fuzzy logic to scientific problems are described, but several texts include more general discussions of the principles and practical implementation of this method. Among the best is Negnevitsky s text on intelligent systems.9... 

Knowledge based approaches such as fuzzy logic, neural networks or multiagents model currently constitute an important axis of research and application in bioprocesses. They have shown their usefulness particularly when one does not have an analytical model but that a certain expertise is available. Harmand and Steyer [37] have addressed that when this expertise comprises a sufficiently important know-how, approaches such as fuzzy logic will be preferred. If, on the other hand, one has only a limited experience but lays out of a rather important data base, the statistical approaches such as neural networks can be used. 

Klir, G. J. and Yuan, B. (1995) Fuzzy sets and fuzzy logic theory and applications. Prentice Hall PTR, Upper Saddle River, NJ. 

The application of fuzzy logic to the risk assessment of the use of solvents in order to evaluate the uncertainties affecting both individual and societal risk estimates is an area with relevance to the present considerations (Bonvicini et al., 1998). In evaluating uncertainty by fuzzy logic, fuzzy numbers describe the uncertain input parameters and calculations are performed using fuzzy arithmetic the outputs will also be fuzzy numbers. The results of these considerations work are an attempt to justify some of the questions the use of fuzzy in the field of risk analysis stimnlates. 

Tanaka, K. (1991) An Introduction to Fuzzy Logic for Practical Applications, Springer-Verlag, New York. 

G.L. Klir, B. Yuan, Fuzzy Sets and Fuzzy Logics-Theory and Application, Prentice Hall, New York, 1995. 

Hollert, H., Heise, S., I udcnz, S., Briiggemann, R., Ahlf, W. and Braunbeck, T. (2002b) Application of a Sediment Quality Triad and different statistical approaches (Hasse diagrams and fuzzy logic) for the comparative evaluation of small streams, Ecotoxicology 11, 311-321. 

While the single-loop PID controller is satisfactory in many process applications, it does not perform well for processes with slow dynamics, time delays, frequent disturbances, or multivariable interactions. We discuss several advanced control methods below 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. 

Fuzzy Logic Control The application 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... 

In many experimental cases, a certain degree of interference occurs among the measures, which gives rise to possible collections of results however, the situation is even more complex if the input data are subjected to uncertainty or imprecision (Kaufmann and Gupta, 1991). Fuzzy logic is the only mathematical application that can properly solve problems with imprecision in input data. 

To accomplish the above two major developments were made in the CAE programs. One development was the application of fuzzy logic in expert software to examine mold-filling simulations for potential... 

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