Fuzzy Logic (Download Seminar Report)
Computer Science Clay Active In SP Posts: 712 Joined: Jan 2009 
30072009, 05:23 PM
With the advent of modern computer technology, the field of Artificial Intelligence is showing a definite utility in all spectrum of life. In the field of control, there is always a need for optimality with improved controller performance. In this paper, the feasibility of Fuzzy Logic as an effective control tool for DC motors is dealt with. This Fuzzy Logic Controller (FLC) is showing a better performance than conventional controllers in the form of increased robustness. In this paper, the role of Fuzzy Logic as a controller and its implementation is studied. INTRODUCTION: Fuzzy logic is a powerful problem solving methodology introduced by Lotfi Zadeh in 1960â„¢s. It provides tools for dealing with imprecision due to uncertainty and vagueness, which is intrinsic to many engineering problems. It is a superset of Boolean or Crisp logic. It emerged into mainstream of information technology in late 1980â„¢s and early 1990 seminar and presentationproject and implimentationsdownloads/?path=/SeminarReports/computer_scienceinformation_technology/seminar and presentationlist7 Use Search at http://topicideas.net/search.php wisely To Get Information About Project Topic and Seminar ideas with report/source code along pdf and ppt presenaion



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FuzzyLogic.ppt (Size: 573.5 KB / Downloads: 570) Prepared by: Shane Warren Brittney Ballard Introduction Definition of fuzzy: Fuzzy – “not clear, distinct, or precise; blurred” Definition of fuzzy logic: A form of knowledge representation suitable for notions that cannot be defined precisely, but which depend upon their contexts 


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37FUZZYLOGIC.pdf (Size: 52.48 KB / Downloads: 486) Fuzzy logic ABSTRACT With the advent of modern computer technology, the field of Artificial Intelligence is showing a definite utility in all spectrum of life. In the field of control, there is always a need for optimality with improved controller performance. In this paper, the feasibility of Fuzzy Logic as an effective control tool for DC motors is dealt with. This Fuzzy Logic Controller (FLC) is showing a better performance than conventional controllers in the form of increased robustness. In this paper, the role of Fuzzy Logic as a controller and its implementation is studied. 


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FuzzyLogic.ppt (Size: 511 KB / Downloads: 287) Fuzzy Logic (Download Seminar Report) Shane Warren Brittney Ballard OVERVIEW What is Fuzzy Logic? Where did it begin? Fuzzy Logic vs. Neural Networks Fuzzy Logic in Control Systems Fuzzy Logic in Other Fields Future 


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Fuzzy Logic[1].doc (Size: 1.22 MB / Downloads: 232) fuzzy logic INTRODUCTION Condition monitoring of induction motors is a fast emerging technology for online detection of incipient faults. It avoids unexpected failure of a critical system. Approximately 30–40% of faults of induction motors are stator faults. This project and implimentation uses fuzzy logic to diagnose various stator faults. A fuzzy logic approach may help to diagnose induction motor faults. In fact, fuzzy logic is remembers of human thinking processes and natural language enabling decisions to be made based on vague information. Therefore, this project and implimentation applies fuzzy logic to induction motors fault detection and diagnosis. The motor condition is described using linguistic variables. Fuzzy subsets and the corresponding membership functions describe stator current amplitudes. A knowledge base, comprising rule and databases, is built to support the fuzzy inference. The induction motor condition is diagnosed using a compositional rule of fuzzy inference. 1.1 PRESENT SCENARIO Induction motor, which is the important workhorse of all industries. Now days this machines are protected by oldest method that is protection is done by relay and circuit breakers. This type implemented in both static and electromagnetic type. These types of protection are done for protecting the machine for over current and over voltage .the main disadvantage is accuracy and reliability. It would sense the fault after few mille seconds; at that short period there is chance for damage in winding and other motor parts. Therefore there is need for accuracy and reliability of operation; therefore it is necessary for implementing new technique in order to protecting the machine due to high cost. There fore we go for the new method for diagnosing the fault occur in the induction motor. Fuzzy logic based fault identification system is one of recent technique with high accuracy and able to identify fault before it would damage the motor. PROJECT DESCRIPTION: The various fault that incurred in the three phase induction motor are identified and diagnose in the initial stage itself .due to involvement of fuzzy logic concept, the accuracy of fault detection get increased. The various fault that are occurred in the threephase induction motor are unbalanced voltage, single phasing, blocked rotor, overload, over voltage, under voltage. The fault, which is above described, would damage the motor, which would occur in short period. Therefore fuzzy logic based algorithm are used to detect the fault and it is done with the help of micro controller PIC 16f877a. The motor characteristics during several fault conditions are predicted using mat lab and the simulation result are shown below. The supply before fed to the threephase induction motor is initially checked by the relay, which is micro controller fed relay. If there is no problem occurred in the supply it will fed to the motor. The basic block diagram for this analysis is shown above. The supply initially fed to the motor is measured by the measuring circuit .The measuring circuit consist of the current transformer and potential transformer and the regulating circuit. From the regulating circuit it is connected to the micro controller the micro controller check the condition of the supply if there is any fault occurs it will give signal to the relay. Then the relay will automatically cut down the supply. The micro controller that has been incorporate here is the PIC 16f877a. The Sensing Circuit Which Consist of CT&PT, measuring circuit and regulating circuit. Which initially senses and regulate the supply voltage and current and it give necessary input to the micro controller circuit .The micro controller execute the fuzzy logic algorithm based program which is to detect the various fault that has going to be occurred and it will sense, it will give necessary input to the LCD and relay circuit. The fault which going to be occurred is initially detected and the corresponding fault are displayed in the LCD display, which is connected to the controller. Therefore this method of fault diagnosis is accurate way of control compare to the Past method and would avoid the damage of the motor 


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Presented by Don Baechtel Introduction to Fuzzy Logic in Artificial Intelligence.ppt (Size: 377 KB / Downloads: 95) Introduction to Fuzzy Logic in Artificial Intelligence Don Baechtel • Architect and Principal Systems Engineer • 33 years experience in Robotics and Industrial Automation • Expert in Process Control, Motion Control, PID Control • Real Time Systems and Industrial Communications • Graphical Programming and Domain Specific Languages • Experience in Fuzzy Logic and Evolutionary Algorithms • Microsoft .Net Technologies • Cloud Computing and Web Oriented Applications • 8 U.S. Patents • Member NEOhioArtificialIntelligenceGroup What Is Fuzzy Logic ? • Form of multivalued logic (algebra) derived from fuzzy set theory. • Designed to deal with reasoning that is approximate rather than accurate. • Consequence of the 1965 proposal of fuzzy set theory by Lotfi Zadeh. • In contrast with "crisp logic", where binary sets have binary logic, fuzzy logic variables may have a truth value that ranges between 0 and 1. • Is not constrained to the two truth values of classic propositional logic. • Can include linguistic variables, like: high, low, hot, cold, and very. Has been applied to many fields, from control theory to artificial intelligence Fuzzy Logic Membership Functions • Membership Functions are subranges of a continuous variable. • Each function maps the same temperature value to a truth value in the 0 to 1 range. • Membership functions can take any shape as long as sum at any point = 100%. • A “crisp” temperature reading converted to ntuple {80% cold, 20% warm, 0% hot}. • May be described as “fairly cold, slightly warm and not hot”. • Membership Function definitions can be adaptively tuned. Fuzzy Set Theory • Fuzzy Set Theory defines fuzzy operators on fuzzy sets. • Fuzzy Logic usually uses IFTHEN rules, or constructs that are equivalent, such as fuzzy associative matrices. • Rules are usually expressed in the form: IF variable IS property THEN action. • There is no "ELSE" – all of the rules are evaluated, because the temperature might be both "cold" and “warm" at the same time to different degrees. • The AND, OR, and NOT operators of Boolean logic exist in fuzzy logic, usually defined as the minimum, maximum, and complement; when they are defined this way, they are called the Zadeh operators. • So for the fuzzy variables x and y: NOT x = (1  truth(x)) x AND y = minimum(truth(x), truth(y)) x OR y = maximum(truth(x), truth(y)) • There are also operators, more linguistic in nature, called hedges that can be applied. These are generally adverbs such as "very", or "somewhat", which modify the meaning of a set. • Multiple overlapping rules provide a “consensus” type output. Example Fuzzy Ruleset A simple Fuzzy temperature regulator that uses a fan might look like this: IF temperature IS very cold THEN stop fan. IF temperature IS cold THEN turn down fan. IF temperature IS comfortable THEN maintain fan speed. IF temperature IS hot THEN speed up fan. Fuzzy Ruleset Evaluation Notice how each rule provides a result as a truth value of a particular membership function for the output variable. In centroid defuzzification the values are OR'd, that is, the maximum value is used and values are not added, and the results are then combined using a centroid calculation. The result or answer(s) provided by the Fuzzy Logic ruleset is a combination or consensus of all of the rules taken together and then frequently converted to a “crisp” value using one of the defuzzification techniques Fuzzy Defuzzification Methods • AI (adaptive integration) DOI 10.1109/ICMNN.1994.593726 • BADD (basic defuzzification distributions) • CDD (constraint decision defuzzification) • COA (center of area) • COG (center of gravity) • ECOA (extended center of area) • EQM (extended quality method) • FCD (fuzzy clustering defuzzification) • FM (fuzzy mean) • FOM (first of maximum) • GLSD (generalized level set defuzzification) • ICOG (indexed center of gravity) • IV (influence value) DOI 10.1109/FUZZY.1996.552647 • LOM (last of maximum) • MeOM (mean of maxima) • MOM (middle of maximum) • QM (quality method) • RCOM (random choice of maximum) • SLIDE (semilinear defuzzification) • WFM (weighted fuzzy mean) Fuzzy Applications • Fuzzy Control Systems • Expert Systems • Fuzzy Databases • Fuzzy Search Engines • Fuzzy Data Mining • Fuzzy Enhanced Programming Languages • Sensor Data Fusion Redundant Systems Artificial Neural Networks The original inspiration for the term Artificial Neural Network came from examination of central nervous systems and their neurons, axons, dendrites and synapses which constitute the processing elements of biological neural networks investigated by neuroscience. In an artificial neural network simple artificial nodes, called variously "neurons", "neurodes", "processing elements" (PEs) or "units", are connected together to form a network of nodes mimicking the biological neural networks — hence the term "artificial neural network". Because neuroscience is still full of questions and because there are many levels of abstraction and many ways to take inspiration from the brain, there is no single formal definition of what an artificial neural network is. Most would agree that it involves a network of simple processing elements which can exhibit complex global behavior determined by the connections between the processing elements and element parameters. While an artificial neural network does not have to be adaptive per se, its practical use comes with algorithms designed to alter the strength (weights) of the connections in the network to produce a desired signal flow. These networks are also similar to the biological neural networks in the sense that functions are performed collectively and in parallel by the units, rather than there being a clear delineation of subtasks to which various units are assigned (see also connectionism). Currently, the term Artificial Neural Network (ANN) tends to refer mostly to neural network models employed in statistics, cognitive psychology and artificial intelligence. Neural network models designed with emulation of the central nervous system (CNS) in mind are a subject of theoretical neuroscience and computational neuroscience. In modern software implementations of artificial neural networks, the approach inspired by biology has been largely abandoned for a more practical approach based on statistics and signal processing. In some of these systems, neural networks or parts of neural networks (such as artificial neurons) are used as components in larger systems that combine both adaptive and nonadaptive elements. While the more general approach of such adaptive systems is more suitable for realworld problem solving, it has far less to do with the traditional artificial intelligence connectionist models. What they do have in common, however, is the principle of nonlinear, distributed, parallel and local processing and adaptation. Differences: Fuzzy Logic vs. Neural Networks 


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Fuzzy Logic
fuzzy logic.docx (Size: 197.23 KB / Downloads: 18) 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. While other approaches require accurate equations to model realworld behaviors, fuzzy design can accommodate the ambiguities of realworld in human language and logic. Although 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 th design of the controller. This makes it easier to mechanize tasks that are already successfully performed by humans. What do you mean 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. Fuzzy Sets Fuzzy Set Theory was formalised by Professor Lofti Zadeh at the University of California in 1965. What Zadeh proposed is very much a paradigm shift that first gained acceptance in the Far East and its successful application has ensured its adoption around the world. A paradigm is a set of rules and regulations which defines boundaries and tells us what to do to be successful in solving problems within these boundaries. For example the use of transistors instead of vacuum tubes is a paradigm shift  likewise the development of Fuzzy Set Theory from conventional bivalent set theory is a paradigm shift. Bivalent Set Theory can be somewhat limiting if we wish to describe a 'humanistic' problem mathematically. For example, Fig 1 below illustrates bivalent sets to characterise the temperature of a room. Fuzzy Set Operations. Union: The membership function of the Union of two fuzzy sets A and B with membership functions and respectively is defined as the maximum of the two individual membership functions. This is called the maximum criterion. 


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Fuzzy Logic
fuzzy logic.docx (Size: 197.23 KB / Downloads: 30) 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. While other approaches require accurate equations to model realworld behaviors, fuzzy design can accommodate the ambiguities of realworld in human language and logic. Although 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 th design of the controller. This makes it easier to mechanize tasks that are already successfully performed by humans. In a broad sense, fuzzy logic refers to fuzzy sets  a set with unsharp boundaries. Examples of fuzzy sets are “hot,” “tall,” “medium,” etc. In a narrow sense, fuzzy logic is a logical system that aims to formalize approximate reasoning .In fuzzy logic a fuzzy symbol can take any truth values from the closed set [0, 1] of real numbers thus generalizing the Boolean truth values. As the technology was further embraced, fuzzy logic was used in more useful applications. In 1987, the first fuzzy logiccontrolled subway was opened in Sendai in northern Japan. Here, fuzzylogic 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. What do you mean 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. Fuzzy Sets Fuzzy Set Theory was formalised by Professor Lofti Zadeh at the University of California in 1965. What Zadeh proposed is very much a paradigm shift that first gained acceptance in the Far East and its successful application has ensured its adoption around the world. A paradigm is a set of rules and regulations which defines boundaries and tells us what to do to be successful in solving problems within these boundaries. For example the use of transistors instead of vacuum tubes is a paradigm shift  likewise the development of Fuzzy Set Theory from conventional bivalent set theory is a paradigm shift. Bivalent Set Theory can be somewhat limiting if we wish to describe a 'humanistic' problem mathematically. For example, Fig 1 below illustrates bivalent sets to characterise the temperature of a room. Fuzzy Set Operations. Union: The membership function of the Union of two fuzzy sets A and B with membership functions and respectively is defined as the maximum of the two individual membership functions. This is called the maximum criterion. 


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Fuzzy Logic
37FUZZYLOGIC.pdf (Size: 52.48 KB / Downloads: 30) INTRODUCTION: Fuzzy logic is a powerful problem solving methodology introduced by Lotfi Zadeh in 1960’s. It provides tools for dealing with imprecision due to uncertainty and vagueness, which is intrinsic to many engineering problems. It is a superset of Boolean or Crisp logic. It emerged into mainstream of information technology in late 1980’s and early 1990 FUZZY LOGIC: Fuzzy logic resembles human decision making with its ability to work from approximate data and find precise solutions. Classical logic or Boolean logic has two values or states. Eg. (true or false). It requires a deep understanding of a system, exact equations, and precise numeric values. Fuzzy logic is a continuous form of logic. eg (bad, very bad, poor, average). It allows modeling complex systems using a higher level of abstraction originating from our knowledge and experience. WORKING OF FUZZY LOGIC: The working of fuzzy logic can be understood by considering a simplified example of a thermostat controlling a heater fan. The room temperature detected through a sensor is input to a controller, which outputs a control force to adjust the heater fan speed. The first step in designing such a fuzzy controller is to characterize the range of values for the input and output variables of the controller. CONVENTIONAL AND FUZZY DESIGNA COMPARISON: The conventional design requires more number of steps than Fuzzy design. Fuzzy logic reduces the design development cycle 


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FUZZY LOGIC
1FUZZY LOGIC.pdf (Size: 205.05 KB / Downloads: 28) INTRODUCTION This is the first in a series of six articles intended to share information and experience in the realm of fuzzy logic (FL) and its application. This article will introduce FL. Through the course of this article series, a simple implementation will be explained in detail. Each article will include additional outside resource references for interested readers. WHERE DID FUZZY LOGIC COME FROM? The concept of Fuzzy Logic (FL) was conceived by Lotfi Zadeh, a professor at the University of California at Berkley, and presented not as a control methodology, but as a way of processing data by allowing partial set membership rather than crisp set membership or nonmembership. This approach to set theory was not applied to control systems until the 70's due to insufficient smallcomputer capability prior to that time. Professor Zadeh reasoned that people do not require precise, numerical information input, and yet they are capable of highly adaptive control. If feedback controllers could be programmed to accept noisy, imprecise input, they would be much more effective and perhaps easier to implement. Unfortunately, U.S. manufacturers have not been so quick to embrace this technology while the Europeans and Japanese have been aggressively building real products around it. WHAT IS FUZZY LOGIC? In this context, FL is a problemsolving control system methodology that lends itself to implementation in systems ranging from simple, small, embedded microcontrollers to large, networked, multichannel PC or workstationbased data acquisition and control systems. It can be implemented in hardware, software, or a combination of both. FL provides a simple way to arrive at a definite conclusion based upon vague, ambiguous, imprecise, noisy, or missing input information. FL's approach to control problems mimics how a person would make decisions, only much faster. HOW IS FL DIFFERENT FROM CONVENTIONAL CONTROL METHODS? FL incorporates a simple, rulebased IF X AND Y THEN Z approach to a solving control problem rather than attempting to model a system mathematically. The FL model is empiricallybased, relying on an operator's experience rather than their technical understanding of the system. For example, rather than dealing with temperature control in terms such as "SP =500F", "T <1000F", or "210C <TEMP <220C", terms like "IF (process is too cool) AND (process is getting colder) THEN (add heat to the process)" or "IF (process is too hot) AND (process is heating rapidly) THEN (cool the process quickly)" are used. These terms are imprecise and yet very descriptive of what must actually happen. Consider what you do in the shower if the temperature is too cold: you will make the water comfortable very quickly with little trouble. FL is capable of mimicking this type of behavior but at very high rate. HOW DOES FL WORK? FL requires some numerical parameters in order to operate such as what is considered significant error and significant rateofchangeoferror, but exact values of these numbers are usually not critical unless very responsive performance is required in which case empirical tuning would determine them. For example, a simple temperature control system could use a single temperature feedback sensor whose data is subtracted from the command signal to compute "error" and then timedifferentiated to yield the error slope or rateofchangeoferror, hereafter called "errordot". Error might have units of degs F and a small error considered to be 2F while a large error is 5F. The "errordot" might then have units of degs/min with a small errordot being 5F/min and a large one being 15F/min. These values don't have to be symmetrical and can be "tweaked" once the system is operating in order to optimize performance. Generally, FL is so forgiving that the system will probably work the first time without any tweaking. SUMMARY FL was conceived as a better method for sorting and handling data but has proven to be a excellent choice for many control system applications since it mimics human control logic. It can be built into anything from small, handheld products to large computerized process control systems. It uses an imprecise but very descriptive language to deal with input data more like a human operator. It is very robust and forgiving of operator and data input and often works when first implemented with little or no tuning. 


