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Fuzzy logic intelligence, control, and information - ebook - free download pdf,epub,mobi,zipdownload from 4shared. Title: Fuzzy Logic Intelligence Control And Information Keywords: Fuzzy Logic Intelligence Control And Information Created Date: 9/8/2014 11:23:32 AM. Fuzzy Logic: Intelligence, Control, and Information, 1/e. Fuzzy Logic For Control Systems. Adjusting to fuzzy design While presenting the paper in Santa Clara, much of the discussion touched on provable control stability. This final issue has discouraged many engineers from employing fuzzy logic in their designs. Despite the great incentive to use fuzzy logic, I found it took me about a year and a half.
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Fuzzy Logic Systems (FLS) produce acceptable but definite output in response to incomplete, ambiguous, distorted, or inaccurate (fuzzy) input.
What is Fuzzy Logic?
Fuzzy Logic (FL) is a method of reasoning that resembles human reasoning. The approach of FL imitates the way of decision making in humans that involves all intermediate possibilities between digital values YES and NO.
The conventional logic block that a computer can understand takes precise input and produces a definite output as TRUE or FALSE, which is equivalent to human’s YES or NO.
The inventor of fuzzy logic, Lotfi Zadeh, observed that unlike computers, the human decision making includes a range of possibilities between YES and NO, such as −
CERTAINLY YES |
POSSIBLY YES |
CANNOT SAY |
POSSIBLY NO |
CERTAINLY NO |
The fuzzy logic works on the levels of possibilities of input to achieve the definite output.
Implementation
- It can be implemented in systems with various sizes and capabilities ranging from small micro-controllers to large, networked, workstation-based control systems.
- It can be implemented in hardware, software, or a combination of both.
Why Fuzzy Logic?
Fuzzy logic is useful for commercial and practical purposes.
- It can control machines and consumer products.
- It may not give accurate reasoning, but acceptable reasoning.
- Fuzzy logic helps to deal with the uncertainty in engineering.
Fuzzy Logic Systems Architecture
It has four main parts as shown −
- Fuzzification Module − It transforms the system inputs, which are crisp numbers, into fuzzy sets. It splits the input signal into five steps such as −
LP | x is Large Positive |
MP | x is Medium Positive |
S | x is Small |
MN | x is Medium Negative |
LN | x is Large Negative |
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- Knowledge Base − It stores IF-THEN rules provided by experts.
- Affinity designer beta 1 7 3 1. Inference Engine − It simulates the human reasoning process by making fuzzy inference on the inputs and IF-THEN rules.
- Defuzzification Module − It transforms the fuzzy set obtained by the inference engine into a crisp value.
The membership functions work on fuzzy sets of variables.
Membership Function
Membership functions allow you to quantify linguistic term and represent a fuzzy set graphically. A membership function for a fuzzy set A on the universe of discourse X is defined as μA:X → [0,1].
Here, each element of X is mapped to a value between 0 and 1. It is called membership value or degree of membership. It quantifies the degree of membership of the element in X to the fuzzy set A.
- x axis represents the universe of discourse.
- y axis represents the degrees of membership in the [0, 1] interval.
There can be multiple membership functions applicable to fuzzify a numerical value. Simple membership functions are used as use of complex functions does not add more precision in the output.
All membership functions for LP, MP, S, MN, and LN are shown as below −
The triangular membership function shapes are most common among various other membership function shapes such as trapezoidal, singleton, and Gaussian.
Here, the input to 5-level fuzzifier varies from -10 volts to +10 volts. Hence the corresponding output also changes.
Example of a Fuzzy Logic System
Let us consider an air conditioning system with 5-level fuzzy logic system. This system adjusts the temperature of air conditioner by comparing the room temperature and the target temperature value.
Algorithm
- Define linguistic Variables and terms (start)
- Construct membership functions for them. (start)
- Construct knowledge base of rules (start)
- Convert crisp data into fuzzy data sets using membership functions. (fuzzification)
- Evaluate rules in the rule base. (Inference Engine)
- Combine results from each rule. (Inference Engine)
- Convert output data into non-fuzzy values. (defuzzification)
Development
Step 1 − Define linguistic variables and terms
Linguistic variables are input and output variables in the form of simple words or sentences. For room temperature, cold, warm, hot, etc., are linguistic terms.
Temperature (t) = {very-cold, cold, warm, very-warm, hot}
Every member of this set is a linguistic term and it can cover some portion of overall temperature values.
Step 2 − Construct membership functions for them
The membership functions of temperature variable are as shown −
Step3 − Construct knowledge base rules
Create a matrix of room temperature values versus target temperature values that an air conditioning system is expected to provide.
RoomTemp. /Target | Very_Cold | Cold | Warm | Hot | Very_Hot |
---|---|---|---|---|---|
Very_Cold | No_Change | Heat | Heat | Heat | Heat |
Cold | Cool | No_Change | Heat | Heat | Heat |
Warm | Cool | Cool | No_Change | Heat | Heat |
Hot | Cool | Cool | Cool | No_Change | Heat |
Very_Hot | Cool | Cool | Cool | Cool | No_Change |
Build a set of rules into the knowledge base in the form of IF-THEN-ELSE structures.
Sr. No. | Condition | Action |
---|---|---|
1 | IF temperature=(Cold OR Very_Cold) AND target=Warm THEN | Heat |
2 | IF temperature=(Hot OR Very_Hot) AND target=Warm THEN | Cool |
3 | IF (temperature=Warm) AND (target=Warm) THEN | No_Change |
Step 4 − Obtain fuzzy value
Fuzzy set operations perform evaluation of rules. The operations used for OR and AND are Max and Min respectively. Combine all results of evaluation to form a final result. This result is a fuzzy value.
Step 5 − Perform defuzzification
Defuzzification is then performed according to membership function for output variable.
Application Areas of Fuzzy Logic
The key application areas of fuzzy logic are as given −
Automotive Systems
- Automatic Gearboxes
- Four-Wheel Steering
- Vehicle environment control
Consumer Electronic Goods
- Hi-Fi Systems
- Photocopiers
- Still and Video Cameras
- Television
Domestic Goods
- Microwave Ovens
- Refrigerators
- Toasters
- Vacuum Cleaners
- Washing Machines
Environment Control
- Air Conditioners/Dryers/Heaters
- Humidifiers
Advantages of FLSs
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- Mathematical concepts within fuzzy reasoning are very simple.
- You can modify a FLS by just adding or deleting rules due to flexibility of fuzzy logic.
- Fuzzy logic Systems can take imprecise, distorted, noisy input information.
- FLSs are easy to construct and understand.
- Fuzzy logic is a solution to complex problems in all fields of life, including medicine, as it resembles human reasoning and decision making.
Disadvantages of FLSs
- There is no systematic approach to fuzzy system designing.
- They are understandable only when simple.
- They are suitable for the problems which do not need high accuracy.
The term fuzzy refers to things which are not clear or are vague. In the real world many times we encounter a situation when we can’t determine whether the state is true or false, their fuzzy logic provides a very valuable flexibility for reasoning. In this way, we can consider the inaccuracies and uncertainties of any situation.
In boolean system truth value, 1.0 represents absolute truth value and 0.0 represents absolute false value. But in the fuzzy system, there is no logic for absolute truth and absolute false value. But in fuzzy logic, there is intermediate value too present which is partially true and partially false.
ARCHITECTURE
Its Architecture contains four parts :
- RULE BASE: It contains the set of rules and the IF-THEN conditions provided by the experts to govern the decision making system, on the basis of linguistic information. Recent developments in fuzzy theory offer several effective methods for the design and tuning of fuzzy controllers. Most of these developments reduce the number of fuzzy rules.
- FUZZIFICATION: It is used to convert inputs i.e. crisp numbers into fuzzy sets. Crisp inputs are basically the exact inputs measured by sensors and passed into the control system for processing, such as temperature, pressure, rpm’s, etc.
- INFERENCE ENGINE: It determines the matching degree of the current fuzzy input with respect to each rule and decides which rules are to be fired according to the input field. Next, the fired rules are combined to form the control actions.
- DEFUZZIFICATION: It is used to convert the fuzzy sets obtained by inference engine into a crisp value. There are several defuzzification methods available and the best suited one is used with a specific expert system to reduce the error.
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Membership function
Definition: A graph that defines how each point in the input space is mapped to membership value between 0 and 1. Input space is often referred as the universe of discourse or universal set (u), which contain all the possible elements of concern in each particular application.
There are largely three types of fuzzifiers:
There are largely three types of fuzzifiers:
Fuzzy Logic Control
- Singleton fuzzifier
- Gaussian fuzzifier
- Trapezoidal or triangular fuzzifier
What is Fuzzy Control?
- It is a technique to embody human-like thinkings into a control system.
- It may not be designed to give accurate reasoning but it is designed to give acceptable reasoning.
- It can emulate human deductive thinking, that is, the process people use to infer conclusions from what they know.
- Any uncertainties can be easily dealt with the help of fuzzy logic.
Advantages of Fuzzy Logic System
- This system can work with any type of inputs whether it is imprecise, distorted or noisy input information.
- The construction of Fuzzy Logic Systems is easy and understandable.
- Fuzzy logic comes with mathematical concepts of set theory and the reasoning of that is quite simple.
- It provides a very efficient solution to complex problems in all fields of life as it resembles human reasoning and decision making.
- The algorithms can be described with little data, so little memory is required.
Disadvantages of Fuzzy Logic Systems
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- Many researchers proposed different ways to solve a given problem through fuzzy logic which lead to ambiguity.There is no systematic approach to solve a given problem through fuzzy logic.
- Proof of its characteristics is difficult or impossible in most cases because every time we do not get mathematical description of our approach.
- As fuzzy logic works on precise as well as imprecise data so most of the time accuracy is compromised.
Application
- It is used in the aerospace field for altitude control of spacecraft and satellite.
- It has used in the automotive system for speed control, traffic control.
- It is used for decision making support systems and personal evaluation in the large company business.
- It has application in chemical industry for controlling the pH, drying, chemical distillation process.
- Fuzzy logic are used in Natural language processing and various intensive applications in Artificial Intelligence.
- Fuzzy logic are extensively used in modern control systems such as expert systems.
- Fuzzy Logic is used with Neural Networks as it mimics how a person would make decisions, only much faster. It is done by Aggregation of data and changing into more meaningful data by forming partial truths as Fuzzy sets.
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