tailieunhanh - Intelligent Control Systems with LabVIEW 2

Giới thiệu Thế giới thực là phức tạp, phức tạp này thường phát sinh từ sự không chắc chắn. Con người có một cách vô thức được khả năng để giải quyết vấn đề phức tạp, mơ hồ, và không chắc chắn nhờ vào những món quà của tư duy. | Chapter 2 Fuzzy Logic Introduction The real world is complex this complexity generally arises from uncertainty. Humans have unconsciously been able to address complex ambiguous and uncertain problems thanks to the gift of thinking. This thought process is possible because humans do not need the complete description of the problem since they have the capacity to reason approximately. With the advent of computers and their increase in computation power engineers and scientists are more and more interested in the creation of methods and techniques that will allow computers to reason with uncertainty. Classical set theory is based on the fundamental concept of a set in which individuals are either a member or not a member. A sharp crisp and ambiguous distinction exists between a member and a non-member for any well-defined set of entities in this theory and there is a very precise and clear boundary to indicate if an entity belongs to a set. Thus in classical set theory an element is not allowed to be in a set 1 or not in a set 0 at the same time. This means that many real-world problems cannot be handled by classical set theory. On the contrary the fuzzy set theory accepts partial membership values Ilf 2 0 1 and therefore in a sense generalizes the classical set theory to some extent. As Prof. Lotfi A. Zadeh suggests by his principle of incompatibility The closer one looks at a real-world problem the fuzzier becomes the solution and thus imprecision and complexity are correlated 1 . Complexity is inversely related to the understanding we can have of a problem or system. When little complexity is presented closed-loop forms are enough to describe the systems. More complex systems need methods such as neural networks that can reduce some uncertainty. When systems are complex enough that only few numerical data exist and the majority of this information is vague fuzzy reasoning can be used for manipulating this information. Industrial Applications The imprecision