Fuzzy System
A Fuzzy System is a Control System based on fuzzy logic.
- AKA: Fuzzy Control System.
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- Example(s):
- See: Digital Data, Control System, Fuzzy Logic, Mathematics, Analog Signal, Mathematical Logic.
References
2017a
- (Sammut & Webb, 2017") ⇒ (2017) "Fuzzy Systems". In: Sammut, C., Webb, G.I. (eds) "Encyclopedia of Machine Learning and Data Mining". Springer, Boston, MA
- QUOTE: A fuzzy system is a computing framework based on the concepts of the theory of fuzzy sets, fuzzy rules, and fuzzy inference. It is structured in four main components: a knowledge base, a fuzzification interface, an inference engine, and a defuzzification interface. The knowledge base consists of a rule base defined in terms of fuzzy rules, and a database that contains the definitions of the linguistic terms for each input and output linguistic variable. The fuzzification interface transforms the (crisp) input values into fuzzy values, by computing their membership to all linguistic terms defined in the corresponding input domain. The inference engine performs the fuzzy inference process, by computing the activation degree and the output of each rule. The defuzzification interface computes the (crisp) output values by combining the output of the rules and performing a specific transformation.
Fuzzy systems can be classified in different categories. The most widely used are the Mamdani and the Takagi-Sugeno models. In a Mamdani fuzzy system the output variables are defined as linguistic variables while in a Takagi-Sugeno fuzzy system they are defined as a linear combination of the input variables.
Fuzzy systems can model nonlinear functions of arbitrary complexity, however, their main strength comes from their ability to represent imprecise concepts and to establish relations between them.
- QUOTE: A fuzzy system is a computing framework based on the concepts of the theory of fuzzy sets, fuzzy rules, and fuzzy inference. It is structured in four main components: a knowledge base, a fuzzification interface, an inference engine, and a defuzzification interface. The knowledge base consists of a rule base defined in terms of fuzzy rules, and a database that contains the definitions of the linguistic terms for each input and output linguistic variable. The fuzzification interface transforms the (crisp) input values into fuzzy values, by computing their membership to all linguistic terms defined in the corresponding input domain. The inference engine performs the fuzzy inference process, by computing the activation degree and the output of each rule. The defuzzification interface computes the (crisp) output values by combining the output of the rules and performing a specific transformation.
2017b
- (Wikipedia, 2017) ⇒ https://en.wikipedia.org/wiki/Fuzzy_control_system Retrieved:2017-7-16.
- A fuzzy control system is a control system based on fuzzy logic — a mathematical system that analyzes analog input values in terms of logical variables that take on continuous values between 0 and 1, in contrast to classical or digital logic, which operates on discrete values of either 1 or 0 (true or false, respectively).[1] [2]
Overview
Fuzzy logic is widely used in machine control. The term "fuzzy" refers to the fact that the logic involved can deal with concepts that cannot be expressed as the "true" or "false" but rather as "partially true". Although alternative approaches such as genetic algorithms and neural networks can perform just as well as fuzzy logic in many cases, fuzzy logic has the advantage that the solution to the problem can be cast in terms that human operators can understand, so that their experience can be used in the design of the controller. This makes it easier to mechanize tasks that are already successfully performed by humans.[1]
- A fuzzy control system is a control system based on fuzzy logic — a mathematical system that analyzes analog input values in terms of logical variables that take on continuous values between 0 and 1, in contrast to classical or digital logic, which operates on discrete values of either 1 or 0 (true or false, respectively).[1] [2]
2010
- (Simoes, 2010) ⇒ Simoes, M. G. (2010). Introduction to fuzzy control. Colorado School of Mines, Engineering Division, Golden, Colorado, 8, 18-22.
- ABSTRACT: In the last few years the applications of artificial intelligence techniques have been used to convert human experience into a form understandable by computers. Advanced control based on artificial intelligence techniques is called intelligent control. Intelligent systems are usually described by analogies with biological systems by, for example, looking at how human beings perform control tasks, recognize patterns, or make decisions. There exists a mismatch between humans and machines: humans reason in uncertain, imprecise, fuzzy ways while machines and the computers that run them are based on binary reasoning. Fuzzy logic is a way to make machines more intelligent enabling them to reason in a fuzzy manner like humans. Fuzzy logic, proposed by Lotfy Zadeh in 1965, emerged as a tool to deal with uncertain, imprecise, or qualitative decision-making problems. Controllers that combine intelligent and conventional techniques are commonly used in the intelligent control of complex dynamic systems. Therefore, embedded fuzzy controllers automate what has traditionally been a human control activity.