fuzzy logic applications full report
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 projectsofme Active In SP Posts: 1,124 Joined: Jun 2010 26-11-2010, 04:10 PM   FUZZY LOGIC.doc (Size: 155.5 KB / Downloads: 124) Presented by:ANSHU ANANDFUZZY LOGIC INTRODUCTION Fuzzy logic has rapidly become one of the most successful of today's technologies for developing sophisticated control systems. The reason for which is very simple. Fuzzy logic addresses such applications perfectly as it resembles human decision making with an ability to generate precise solutions from certain or approximate information. It fills an important gap in engineering design methods left vacant by purely mathematical approaches (e.g. linear control design), and purely logic-based approaches (e.g. expert systems) in system design. While other approaches require accurate equations to model real-world behaviors, fuzzy design can accommodate the ambiguities of real-world human language and logic. It provides both an intuitive method for describing systems in human terms and automates the conversion of those system specifications into effective models. WHAT REALLY FUZZY LOGIC OFFERS- The first applications of fuzzy theory were primaly industrial, such as process control for cement kilns. However, as the technology was further embraced, fuzzy logic was used in more useful applications. In 1987, the first fuzzy logic-controlled subway was opened in Sendai in northern Japan. Here, fuzzy-logic controllers make subway journeys more comfortable with smooth braking and acceleration. Best of all, all the driver has to do is push the start button! Fuzzy logic was also put to work in elevators to reduce waiting time. Since then, the applications of Fuzzy Logic technology have virtually exploded, affecting things we use everyday. Take for example, the fuzzy washing machine . A load of clothes in it and press start, and the machine begins to churn, automatically choosing the best cycle. The fuzzy microwave, Place chili, potatoes, or etc in a fuzzy microwave and push single button, and it cooks for the right time at the proper temperature. The fuzzy car, manuvers itself by following simple verbal instructions from its driver. It can even stop itself when there is an obstacle immedeately ahead using sensors. But, practically the most exciting thing about it, is the simplicity involved in operating it. WHAT DO YOU MEAN BY FUZZY Before illustrating the mechanisms which make fuzzy logic machines work, it is important to realize what fuzzy logic actually is. Fuzzy logic is a superset of conventional(Boolean) logic that has been extended to handle the concept of partial truth- truth values between "completely true" and "completely false". As its name suggests, it is the logic underlying modes of reasoning which are approximate rather than exact. The importance of fuzzy logic derives from the fact that most modes of human reasoning and especially common sense reasoning are approximate in nature. The essential characteristics of fuzzy logic as founded by Zader Lotfi are as follows. In fuzzy logic, exact reasoning is viewed as a limiting case of approximate reasoning. In fuzzy logic everything is a matter of degree. Any logical system can be fuzzified In fuzzy logic, knowledge is interpreted as a collection of elastic or, equivalently , fuzzy constraint on a collection of variables Inference is viewed as a process of propagation of elastic constraints. The third statement hence, define Boolean logic as a subset of Fuzzy logic. RULES OF FUZZY LOGIC- Human beings make descisions based on rules. Although, we may not be aware of it, all the descisions we make are all based on computer like if-then statements. If the weather is fine, then we may decide to go out. If the forecast says the weather will be bad today, but fine tommorow, then we make a descision not to go today, and postpone it till tommorow. Rules associate ideas and relate one event to another. Fuzzy machines, which always tend to mimic the behaviour of man, work the same way. However, the descision and the means of choosing that descision are replaced by fuzzy sets and the rules are replaced by fuzzy rules. Fuzzy rules also operate using a series of if-then statements. For instance, if X then A, if y then b, where A and B are all sets of X and Y. Fuzzy rules define fuzzy patches, which is the key idea in fuzzy logic. A machine is made smarter using a concept designed by Bart Kosko called the Fuzzy Approximation Theorem (FAT). The FAT theorem generally states a finite number of patches can cover a curve as seen in the figure below. If the patches are large, then the rules are sloppy. If the patches are small then the rules are fine. FUZZY PATCHES In a fuzzy system this simply means that all our rules can be seen as patches and the input and output of the machine can be associated together using these patches. Graphically, if the rule patches shrink, our fuzzy subset triangle gets narrower. Simple enough? Yes, because even novices can build control systems that beat the best math models of control theory. Naturally, it is math-free system. FUZZY CONTROL Fuzzy control, which directly uses fuzzy rules is the most important application in fuzzy theory. Using a procedure originated by Ebrahim Mamdani in the late 70s, three steps are taken to create a fuzzy controlled machine: Fuzzification(Using membership functions to graphically describe a situation) Rule evaluation(Application of fuzzy rules) Defuzzification(Obtaining the crisp or actual results) As a simple example on how fuzzy controls are constructed, consider the following classic situation: the inverted pendulum. Here, the problem is to balance a pole on a mobile platform that can move in only two directions, to the left or to the right. The angle between the platform and the pendulum and the angular velocity of this angle are chosen as the inputs of the system. The speed of the platform hence, is chosen as the corresponding output. Step 1 First of all, the different levels of output (high speed, low speed etc.) of the platform is defined by specifying the membership functions for the fuzzy_sets. The graph of the function is shown below Similary, the different angles between the platform and the pendulum and... the angular velocities of specific angles are also defined Note: For simplicity, it is assumed that all membership functions are spreaded equally. Hence, this explains why no actual scale is included in the graphs. Step 2 The next step is to define the fuzzy rules. The fuzzy rules are mearly a series of if-then statements as mentioned above. These statements are usually derived by an expert to achieve optimum results. Some examples of these rules are: i) If angle is zero and angular velocity is zero then speed is also zero. ii) If angle is zero and angular velocity is low then the speed shall be low. The full set of rules is summarised in the table below. The dashes are for conditions, which have no rules ascociated with them. This is to simplify the situation.
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