In this term paper we discuss all formulation of fuzzy automata and also working on fuzzy automata as model of learning system. In this paper we discuss some applications comes under in fuzzy automata theory. Theory of fuzzy sets and fuzzy theory logic has been applied to problem in various fields like topologies, game theory etc also we discuss or described in this term paper. And in this term paper we have also discuss its future work and where we use its applications. There is a deep season to study fuzzy automata: several languages are fuzzy by nature (e. G. : the language containing words in which many letter “a” occur.
That type of stuff comes under in fuzzy automata and all these terms and useful application we all discuss Mathematical models in classical computation automata have been an important area in theoretical computer science. The basic idea in the formulation of a fuzzy automaton is that, unlike the classical case, the fuzzy automaton can switch from one state to another one to certain (truth) degree. Thus, researching fuzzy automaton tit ability of processing fuzzy processes is need. Even when a system input at a time is missing, the system can work accurately.
Fuzzy automata are the machines accepting fuzzy regular language. This language is a feature of fuzzy language and is described by fuzzy regular expression. Many decision- making and problem- solving tasks are too complex to be understood quantitatively, however people succeed by using knowledge that is imprecise rather than precise. Fuzzy automata theory resembles human reasoning in its use of approximate information and uncertainty to generate decision. In here we simulate real world problems such as decision making problem in urban traffic system, where a condition of Jam can have cumulative effect on city commutation structure.
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The learning section primarily consists Figure 1: Basic Learning Model of composite fuzzy automaton. The performance evaluator serves as an unreliable “teacher” who tries to teach the “student” (the learning section and decision maker) to make correct decisions. The decision executed by the decision maker is deterministic. Since online operations are required, the decision will be based on the maximum membership. In here we discuss some applications of fuzzy automata some are under below: Pattern classification and control system: The learning model proposed above has been applied to engineering problems.
We consider its application to pattern classification and control system. Figure 2: Learning pattern classifier The pattern classifier receives a new sample from the unknown environment during each time interval. After the new sample is processed through the receptor, the output is fed to both the decision maker for classification and the performance valuator for performance evaluation. The performance criterion of the system has to be selected so that its minimization or minimization reflects the clustering properties of the pattern classes, I. E. The unknown environment.
Because of the natural distribution of the samples, the performance criterion can be in corporate into the system to serve as a teacher of the learning pattern recognizer. Control urban traffic transport system: In here we use fuzzy automata neural network application model to through this model (FAN) we control the urban traffic transport problem. Figure 3: FAN framework In a view of the presence on urban transport system there is an urgent need of accurate and effective urban traffic model. Whenever each an every crossing across the road network is interconnected by the control section.
The control section acts as control nervous system for lighting controls. Simi to Sin represents signal control and feedback given from cameras installed at Junctions. Figure 4: FAN generated signal control It then goes to FAN framework which consists of “performance evaluator” and “feature extraction” which takes the help of previously stored samples of similar situation. In case there is a Jamming condition in one of the samples of Pl (k), the FAN generates peak estimation, which is then fed for De-fuzzier and finally applied to control system where an algorithm is being developed.
A fuzzy automaton has been applied to problem in a variety of fields: 1. Taxonomy 4. Logic 7. Medicine 2. Topologies 5. Automata theory 8. Law 3. Information retrieval 6. Game theory 9. Decision support Recently fuzzy machine have developed including: 1 . Automatic train control 3. Tunnel digging machinery 5. Washing machines 2. Rice cooker 4. Vacuum cleaner 6. Air conditioner In fuzzy application under extrapolates washing machine 1200 RPM: The extrapolates washing machine has a number feature which will make life easier for you: Foam detection Imbalance compensation Washing without washing – with automatic water level adjustment.