Microsoft Word The use of Fuzzy Logic Control in Manufacturing Systems docx
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TheuseofFuzzyLogicControlinManufacturingSystems
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- Fuzzy Logic Fuzzy Inference System
Introduction:
In most of the cases, it is important to deliver the manufactured products at the exact time that was agreed upon with the customer. So, we need to know how many machines and how many employees to have for this specific job. We do not want to have more employees or more machines because this will cost the company more money. On the other hand having less machines or employees will cause the delivery to be delayed. The same token can be applied in the case of a bank where we need to determine how many tellers we need to have at different times depending on the number of customers. Our purpose in both cases is to accomplish the task of the production of all the products or the service of all the customers in the time constraint we are given. The number of machines should be higher than the calculated numbers because there is a possibility that some of these machines will fail down. This project is mainly about controlling the number of machines needed to operate in order to finish a specified job with a given number of products (queue). The operation time is assumed to be an eight hour regular day or night shift time. The service time is also assumed to be constant. We will design our system using fuzzy logic inference system. The proposed system will have to parameters; the number of the products and the service time and two outputs; the number of machine needed and the number of the operators, whose jobs is to watch these machines for any expected failures. 2 Fuzzy Logic & Fuzzy Inference System: Fuzzy logic was first introduced by Lotfi Zadeh proposal of fuzzy set theory in 1965. Since then, fuzzy logic has been developed and more applications for fuzzy set theory were developed. Fuzzy logic is a form of that has more than two values, it was derived from fuzzy set theory to deal with approximate logic rather than real facts. In contrast with "crisp logic", where binary sets have binary logic, fuzzy logic variables may have a logical value that ranges between 0 and 1 and is not limited to only to two values (0, 1) like in the binary logic. Furthermore, when linguistic variables are used, these degrees may be managed by specific functions usually called membership functions [1] Fuzzy inference is the concept of using the fuzzy logic to map the output for each fuzzy input. The mapping then provides a reference from which we can make the decisions about what the output will be. The process of fuzzy inference requires the use of all pieces Membership Functions, Logical Operations (mostly or/and), and If-Then Rules. There are different types of fuzzy inference system. In this project we will use the mamdani fuzzy inference system. [2] Fuzzy inference systems have been successfully applied in different fields of industry such as automatic control, data classification, decision analysis, expert systems, and computer vision. Fuzzy inference systems have different names, such as fuzzy-rule-based systems, fuzzy expert systems, fuzzy modeling, fuzzy associative memory, fuzzy logic controllers, and simply (and ambiguously) fuzzy systems. [3] Mamdani's fuzzy inference method is the most commonly used fuzzy approach. Mamdani's method was among the first control systems built using fuzzy set theory. It was found in 1975 by Ebrahim Mamdani as an attempt to control a steam engine and boiler combination by synthesizing a set of linguistic control rules obtained from experienced human operators. Mamdani’s method was based on Lotfi Zadeh's 1973 paper on fuzzy algorithms for complex systems and decision processes. Mamdani-type inference uses piecewise linear membership functions; triangular fuzzy numbers and trapezoidal fuzzy numbers. [3,4] The common way to specify a TFN in analytical form is to write the following. 3 The common way to express the general form for a TrFN is the following: Download 110.32 Kb. Do'stlaringiz bilan baham: |
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