Exploring how fuzzy logic enhances AI, systems thinking, and real-world applications
Fuzzy logic is an extension of classical logic and makes it possible to deal with imprecise and uncertain data. Compared to traditional logic, which is based on things being either true or false, fuzzy logic also allows for states in between. This means that states can be “partially true” or “somewhat false”.
In this article, we take a detailed look at fuzzy logic, its concepts, and fundamentals and try to use examples that are as simple as possible. We also show the advantages and disadvantages of using this method and explain how it differs from traditional logic.
Fuzzy logic is an extension of classical Boolean logic which, in addition to the two states true/false or 0/1, also allows statements that are only true or false to a certain degree and therefore assume a value between 0 and 1. This concept was invented in the 1960s by Lotfi Zadeh at the University of California. He tried to solve the problem of teaching a machine to speak human, natural language and discovered that language cannot be classified into 0 and 1.
