- Application Of Fuzzy Logic In Washing Machine Ppt
- Application Of Fuzzy Logic Pdf
- Fuzzy Logic In Ai
- Application Of Fuzzy Logic In Image Processing Ppt
- Application Of Fuzzy Logic In Engineering
- Fuzzy Logic Application
- Fuzzy Logic And Its Applications
What Is Fuzzy Logic?
The term fuzzy mean things which are not very clear or vague. In real life, we may come across a situation where we can't decide whether the statement is true or false. At that time, fuzzy logic offers very valuable flexibility for reasoning. We can also consider the uncertainties of any situation.
Fuzzy logic algorithm helps to solve a problem after considering all available data. Then it takes the best possible decision for the given the input. The FL method imitates the way of decision making in a human which consider all the possibilities between digital values T and F.
In this tutorial, you will learn
And the co-editor of Fuzzy Logic and Probability Applications: Bridging the Gap.His sabbatical leaves in 2001–2002 at the University of Calgary, Alberta, Canada, and most recently in 2008–2009 at Gonzaga University in Spokane, Washington, have resulted in. Application of Logistic Regression Analysis to Fuzzy Cognitive Maps (Vesa A Niskanen) Fuzzy Logic in Medicine (Yutaka Hata) Readership: Researchers, academics, professionals, graduate and undergraduate students in fuzzy logic and its applications.
History of Fuzzy Logic
Although, the concept of fuzzy logic had been studied since the 1920's. The term fuzzy logic was first used with 1965 by Lotfi Zadeh a professor of UC Berkeley in California. He observed that conventional computer logic was not capable of manipulating data representing subjective or unclear human ideas.
Fuzzy logic has been applied to various fields, from control theory to AI. It was designed to allow the computer to determine the distinctions among data which is neither true nor false. Something similar to the process of human reasoning. Like Little dark, Some brightness, etc.
Characteristics of Fuzzy Logic
Here, are some important characteristics of fuzzy logic:
- Flexible and easy to implement machine learning technique
- Helps you to mimic the logic of human thought
- Logic may have two values which represent two possible solutions
- Highly suitable method for uncertain or approximate reasoning
- Fuzzy logic views inference as a process of propagating elastic constraints
- Fuzzy logic allows you to build nonlinear functions of arbitrary complexity.
- Fuzzy logic should be built with the complete guidance of experts
When not to use fuzzy logic
However, fuzzy logic is never a cure for all. Therefore, it is equally important to understand that where we should not use fuzzy logic.
Here, are certain situations when you better not use Fuzzy Logic:
- If you don't find it convenient to map an input space to an output space
- Fuzzy logic should not be used when you can use common sense
- Many controllers can do the fine job without the use of fuzzy logic
Fuzzy Logic Architecture
Fuzzy Logic architecture has four main parts as shown in the diagram:
Rule Base:
Application Of Fuzzy Logic In Washing Machine Ppt
It contains all the rules and the if-then conditions offered by the experts to control the decision-making system. The recent update in fuzzy theory provides various methods for the design and tuning of fuzzy controllers. This updates significantly reduce the number of the fuzzy set of rules.
Fuzzification:
Fuzzification step helps to convert inputs. It allows you to convert, crisp numbers into fuzzy sets. Crisp inputs measured by sensors and passed into the control system for further processing. Like Room temperature, pressure, etc.
Inference Engine:
Questions. Esonic g41 motherboard driver. Description.
It helps you to determines the degree of match between fuzzy input and the rules. Based on the % match, it determines which rules need implment according to the given input field. After this, the applied rules are combined to develop the control actions.
Defuzzification:
At last the Defuzzification process is performed to convert the fuzzy sets into a crisp value. There are many types of techniques available, so you need to select it which is best suited when it is used with an expert system.
Fuzzy Logic vs. Probability
Application Of Fuzzy Logic Pdf
Fuzzy Logic | Probability |
Fuzzy: Tom's degree of membership within the set of old people is 0.90. | Probability: There is a 90% chance that Tom is old. |
Fuzzy logic takes truth degrees as a mathematical basis on the model of the vagueness phenomenon. | Probability is a mathematical model of ignorance. |
Crisp vs. Fuzzy
Crisp | Fuzzy |
It has strict boundary T or F | Fuzzy boundary with a degree of membership |
Some crisp time set can be fuzzy | It can't be crisp |
True/False {0,1} | Membership values on [0,1] |
In Crisp logic law of Excluded Middle and Non- Contradiction may or may not hold | In the fuzzy logic law of Excluded Middle and Non- Contradiction hold |
Classical Set vs. Fuzzy set Theory
Classical Set | Fuzzy Set Theory |
Classes of objects with sharp boundaries. | Classes of objects do not have sharp boundaries. |
A classical set is defined by crisp boundaries, i.e., there is clarity about the location of the set boundaries. | A fuzzy set always has ambiguous boundaries, i.e., there may be uncertainty about the location of the set boundaries. |
Widely used in digital system design | Used only in fuzzy controllers. |
Fuzzy Logic Examples
Fuzzy Logic In Ai
See the below-given diagram. It shows that in fuzzy systems, the values are denoted by a 0 to 1 number. In this example, 1.0 means absolute truth and 0.0 means absolute falseness.
Application Areas of Fuzzy Logic
The Blow given table shows how famous companies using fuzzy logic in their products.Product | Company | Fuzzy Logic |
Anti-lock brakes | Nissan | Use fuzzy logic to controls brakes in hazardous cases depend on car speed, acceleration, wheel speed, and acceleration |
Auto transmission | NOK/Nissan | Fuzzy logic is used to control the fuel injection and ignition based on throttle setting, cooling water temperature, RPM, etc. |
Auto engine | Honda, Nissan | Use to select geat based on engine load, driving style, and road conditions. |
Copy machine | Canon | Using for adjusting drum voltage based on picture density, humidity, and temperature. |
Cruise control | Nissan, Isuzu, Mitsubishi | Use it to adjusts throttle setting to set car speed and acceleration |
Dishwasher | Matsushita | Use for adjusting the cleaning cycle, rinse and wash strategies based depend upon the number of dishes and the amount of food served on the dishes. |
Elevator control | Fujitec, Mitsubishi Electric, Toshiba | Use it to reduce waiting for time-based on passenger traffic |
Golf diagnostic system | Maruman Golf | Selects golf club based on golfer's swing and physique. |
Fitness management | Omron | Fuzzy rules implied by them to check the fitness of their employees. |
Kiln control | Nippon Steel | Mixes cement |
Microwave oven | Mitsubishi Chemical | Sets lunes power and cooking strategy |
Palmtop computer | Hitachi, Sharp, Sanyo, Toshiba | Recognizes handwritten Kanji characters |
Plasma etching | Mitsubishi Electric | Sets etch time and strategy |
Advantages of Fuzzy Logic System
- The structure of Fuzzy Logic Systems is easy and understandable
- Fuzzy logic is widely used for commercial and practical purposes
- It helps you to control machines and consumer products
- It may not offer accurate reasoning, but the only acceptable reasoning
- It helps you to deal with the uncertainty in engineering
- Mostly robust as no precise inputs required
- It can be programmed to in the situation when feedback sensor stops working
- It can easily be modified to improve or alter system performance
- inexpensive sensors can be used which helps you to keep the overall system cost and complexity low
- It provides a most effective solution to complex issues
Application Of Fuzzy Logic In Image Processing Ppt
Disadvantages of Fuzzy Logic Systems
- Fuzzy logic is not always accurate, so The results are perceived based on assumption, so it may not be widely accepted.
- Fuzzy systems don't have the capability of machine learning as-well-as neural network type pattern recognition
- Validation and Verification of a fuzzy knowledge-based system needs extensive testing with hardware
- Setting exact, fuzzy rules and, membership functions is a difficult task
- Some fuzzy time logic is confused with probability theory and the terms
Summary
- The term fuzzy mean things which are not very clear or vague
- The term fuzzy logic was first used with 1965 by Lotfi Zadeh a professor of UC Berkeley in California
- Fuzzy logic is a flexible and easy to implement machine learning technique
- Fuzzy logic should not be used when you can use common sense
- Fuzzy Logic architecture has four main parts 1) Rule Basse 2) Fuzzification 3) Inference Engine 4) Defuzzification
- Fuzzy logic takes truth degrees as a mathematical basis on the model of the vagueness while probability is a mathematical model of ignorance
- Crisp set has strict boundary T or F while Fuzzy boundary with a degree of membership
- A classical set is widely used in digital system design while fuzzy set Used only in fuzzy controllers
- Auto transmission, Fitness management, Golf diagnostic system, Dishwasher, Copy machine are some applications areas of fuzzy logic
- Fuzzy logic helps you to control machines and consumer products
Description of Fuzzy Logic
In recent years, the number and variety of applications of fuzzy logic have increased significantly. The applications range from consumer products such as cameras, camcorders, washing machines, and microwave ovens to industrial process control, medical instrumentation, decision-support systems, and portfolio selection.
To understand why use of fuzzy logic has grown, you must first understand what is meant by fuzzy logic.
Fuzzy logic has two different meanings. In a narrow sense, fuzzy logic is a logical system, which is an extension of multivalued logic. However, in a wider sense fuzzy logic (FL) is almost synonymous with the theory of fuzzy sets, a theory which relates to classes of objects with unsharp boundaries in which membership is a matter of degree. In this perspective, fuzzy logic in its narrow sense is a branch of FL. Even in its more narrow definition, fuzzy logic differs both in concept and substance from traditional multivalued logical systems.
In Fuzzy Logic Toolbox™ software, fuzzy logic should be interpreted as FL, that is, fuzzy logic in its wide sense. The basic ideas underlying FL are explained in Foundations of Fuzzy Logic. What might be added is that the basic concept underlying FL is that of a linguistic variable, that is, a variable whose values are words rather than numbers. In effect, much of FL may be viewed as a methodology for computing with words rather than numbers. Although words are inherently less precise than numbers, their use is closer to human intuition. Furthermore, computing with words exploits the tolerance for imprecision and thereby lowers the cost of solution.
Another basic concept in FL, which plays a central role in most of its applications, is that of a fuzzy if-then rule or, simply, fuzzy rule. Although rule-based systems have a long history of use in Artificial Intelligence (AI), what is missing in such systems is a mechanism for dealing with fuzzy consequents and fuzzy antecedents. In fuzzy logic, this mechanism is provided by the calculus of fuzzy rules. The calculus of fuzzy rules serves as a basis for what might be called the Fuzzy Dependency and Command Language (FDCL). Although FDCL is not used explicitly in the toolbox, it is effectively one of its principal constituents. In most of the applications of fuzzy logic, a fuzzy logic solution is, in reality, a translation of a human solution into FDCL.
A trend that is growing in visibility relates to the use of fuzzy logic in combination with neurocomputing and genetic algorithms. More generally, fuzzy logic, neurocomputing, and genetic algorithms may be viewed as the principal constituents of what might be called soft computing. Unlike the traditional, hard computing, soft computing accommodates the imprecision of the real world. The guiding principle of soft computing is: Exploit the tolerance for imprecision, uncertainty, and partial truth to achieve tractability, robustness, and low solution cost. In the future, soft computing could play an increasingly important role in the conception and design of systems whose MIQ (Machine IQ) is much higher than that of systems designed by conventional methods.
Among various combinations of methodologies in soft computing, the one that has highest visibility at this juncture is that of fuzzy logic and neurocomputing, leading to neuro-fuzzy systems. Within fuzzy logic, such systems play a particularly important role in the induction of rules from observations. An effective method developed by Dr. Roger Jang for this purpose is called ANFIS (Adaptive Neuro-Fuzzy Inference System). This method is an important component of the toolbox.
Fuzzy logic is all about the relative importance of precision: How important is it to be exactly right when a rough answer will do?
You can use Fuzzy Logic Toolbox software with MATLAB® technical computing software as a tool for solving problems with fuzzy logic. Fuzzy logic is a fascinating area of research because it does a good job of trading off between significance and precision — something that humans have been managing for a very long time.
In this sense, fuzzy logic is both old and new because, although the modern and methodical science of fuzzy logic is still young, the concepts of fuzzy logic relies on age-old skills of human reasoning.
Application Of Fuzzy Logic In Engineering
Fuzzy logic is a convenient way to map an input space to an output space. Mapping input to output is the starting point for everything. Consider the following examples:
- With information about how good your service was at a restaurant, a fuzzy logic system can tell you what the tip should be.
- With your specification of how hot you want the water, a fuzzy logic system can adjust the faucet valve to the right setting.
- With information about how far away the subject of your photograph is, a fuzzy logic system can focus the lens for you.
- With information about how fast the car is going and how hard the motor is working, a fuzzy logic system can shift gears for you.
Fuzzy Logic Application
A graphical example of an input-output map is shown in the following figure.
Determining the appropriate amount of tip requires mapping inputs to the appropriate outputs. Between the input and the output, the preceding figure shows a black box that can contain any number of things: fuzzy systems, linear systems, expert systems, neural networks, differential equations, interpolated multidimensional lookup tables, or even a spiritual advisor, just to name a few of the possible options. Clearly the list could go on and on.
Fuzzy Logic And Its Applications
Of the dozens of ways to make the black box work, it turns out that fuzzy is often the very best way. Why should that be? As Lotfi Zadeh, who is considered to be the father of fuzzy logic, once remarked: 'In almost every case you can build the same product without fuzzy logic, but fuzzy is faster and cheaper.'