Jenny Carter - Fuzzy Logic(2021)[Carter et al][9783030664749]

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Many problems within industry and commerce can be modelled mathematically and/or statistically. However, in practice, applications modelled in this way often perform poorly. Implemented assumptions can cause developed solutions to lack a certain level of robustness. Standard control applications, for example, often work poorly under certain conditions or they are not smooth in their movement. Physical measurements are, by their nature, imprecise. They are only as good as the instrument doing the measuring. A 2 Kg bag of sugar is never exactly 2 Kg for example. Yet, traditional mathematically based control solutions use such measurements as being precise. Experts make decisions with imprecise data in an uncertain world. They work with knowledge that is rarely defined mathematically or algorithmically but uses vague terminology with words.

Fuzzy logic relies on the concept of a fuzzy set, which was proposed by Lotfi Zadeh, in his 1965 seminal paperFuzzy Sets (published in Information and Control, volume 8, pp. 338353). Zadeh was a Professor at the University of Southern California until his death in 2017. The idea of fuzzy sets described in his seminal work lays the basis for Fuzzy Logic. Fuzzy Logic is particularly good at handling uncertainty, vagueness and imprecision. This is very useful where a problem can be described linguistically (using words) or, as with neural networks, where there is data and you are looking for relationships or patterns within that data. Fuzzy Logic uses imprecision to provide robust solutions to problems. Applications of fuzzy logic are varied and include robotics, washing machine control, nuclear reactors, information retrieval, train scheduling, system modelling, camera focus, stock tracking.

The chapters in this book provide further insight into the wide range of approaches to problem-solving using fuzzy logic and illustrate these approaches over a wide variety of application areas.

The Editors

The subject of this chapter is the foundations of Fuzzy Set Theory and Fuzzy Logic. When applying techniques in computer science it is not necessary to know their historical development. Like driving a car, it is not necessary to understand the workings of the internal combustion engine. On the other hand, understanding engines and their history can enhance our driving experience and one gains a broader appreciation of the car as an object created by human endeavour. Similarly, when we stand back and view Fuzzy Logic from a historical perspective, by examining its foundations we gain an overview that increases our ability to see relationships, and allows us to explore options for future innovation. This chapter is intended to give the reader a path through the literature to help gain a historical perspective. This is not intended to be a comprehensive review, rather to inspire further reading; [] a single source text covering the historical development of Fuzzy Set Theory and Fuzzy Logic, is a good place to start. My conclusions propose some blue sky ideas which are ultimately intended to pique interest and encourage further thought. As with so many other subjects, it all starts with the Greeks and Aristotle.