Introduction to Fuzzy Logic
What Is Fuzzy Logic?
Fuzzy logic helps in solving a particular problem after considering all the available data and then taking the suitable decision. The fuzzy logic method emulates the human way of decision making, which considers all the possibilities between digital values of True and False.
Fuzzy logic can be implemented in systems with different sizes and capabilities. For implementation, there should be a range of micro to macro controllers. Moreover, it can also be implemented in hardware or software or in a combination of both in Artificial Intelligence.
Fuzzy Logic Systems Architecture
Basically, there are four parts in the architecture of the fuzzy logic system-
- Rule Base: It contains all the rules and “if-then” conditions offered by experts to control decision-making. The most recent update in fuzzy logic provides a number of methods for the design and tuning. Moreover, the update has significantly reduced the number of sets of rules.
- Fuzzification: Fuzzification is the second in this series and it helps to convert inputs. It helps in converting crisp numbers to fuzzy sets. Crisp inputs are measured by sensors and passed into a control system for processing. The module is used to transform inputs of the system and also helps in splitting the input signals into five steps:
— LP: x is a large positive.
— MP: x is a medium positive.
— S: x is small.
— MN: x is medium negative.
— LN: x is large negative.
- Inference Engine: The third one helps in determining the degree of match between fuzzy inputs and fuzzy rules. Based on that percentage it decides which rule is needed to be implemented. After it, to develop the control actions, applied rules are combined. Generally, the process helps in simulating the human reasoning process and that’s by making fuzzy inference on the inputs and “if-then” rules.
- Defuzzification: In this module, the transformation of a fuzzy set into crisp value takes place. There are a number of techniques available to do so, and it’s up to the programmer to select the best from the available ones.
- Linguistic Variables: Linguistic variables are basically the input and output variables of the system. The values of these variables are mostly words and sentences from the natural languages and no numeric value. Linguistic variables can be decomposed into a set of linguistic terms.
Example: Consider an air conditioner
In this, temperature (t) is a linguistic variable representing the temperature of a room. Terms such as hot and cold are used as per real-life examples to qualify the temperature. These both are linguistic temperature values.
Now, T(t) = (too cold, too hot, cold, hot). These can be the outcomes or say a set of decompositions for the linguistic temperature. Each set of this decomposition is called a linguistic term and covers a portion of overall temperature value.
Membership functions are used in the second and fourth parts of the fuzzy logic architecture to map the non-fuzzy values to linguistic terms and vice-versa. Basically, to quantify a linguistic term a membership function is used.
Fuzzy logic Controller
The information used by people in their daily life is to make and implement easily. Common thumb rules can be applied to those control conditions according to demand. To combat the unwanted effect of system feedback, gaining knowledge is the only powerful weapon.
The fuzzy logic controller uses a flexible set of rules. It is then applied with proper membership work specifying values.
Use of Fuzzy Logic?
Generally, fuzzy logic systems can be used for practical and commercial purposes. Also, it can be used in –
- Consumer products and control machines
- Dealing with uncertainty in engineering
- Giving at least acceptable reasoning if not accurate reasoning.
Apart from this, fuzzy logic offers various unique features that make it a perfect choice for controlling many problems:
1) It is intrinsically robust since it does not require fixed, noise-free inputs and can be programmed to fail safely if a feedback sensor quits or is destroyed. Despite a wide range of input variations, output control is the smooth control function.
2) Fuzzy logic controller process user-defined rules and override the target control system. It can be altered easily to improve or boost system performance. By generating appropriate governing rules, new sensors can be easily generated into the system.
3) Fuzzy logic is not limited to only one or two control outputs or few feedback inputs. Also, it’s not necessary to measure change rate parameters before its implementation. Data from sensors that provide some system indication is enough. This will allow the sensors to be inexpensive and keeping the system cost low for the overall setup.
4) Due to the rule-based operation, inputs of reasonable numbers like (1–6 or more) and numerous outputs like (1–4) can be generated. Although, defining the rules may become complex in case too many inputs and outputs are chosen for single implementation, since after defining rules, their interrelations must be defined. Also, it will be a good idea to break the control system in smaller portions and use multiple fuzzy logic controllers distributed on the system, with each one carrying more responsibilities.
5) Fuzzy logic is capable of managing non-linear systems that would be difficult to manage mathematically. This will open the doors for the control system and it would be easy to gauge feasibility for automation.
Characteristics of Fuzzy Logic
Have a look at a few important characteristics of fuzzy logic:
- Very flexible and easy to implement
- Helps in mimic of the logic of human thoughts
- Allows a person to build non-linear function of arbitrary complexity
- Build with complete guidance of experts
- In fuzzy logic, interference is a process of propagating elastic constraints
- Highly suitable method for uncertain reasoning
When should fuzzy logic not be used?
Fuzzy logic is never a one-size-fits-all solution. So, it’s important to know in which conditions one should not use fuzzy logic.
Have a look at the situations:
- If it’s not easy for a person to map input space to an output space.
- Fuzzy logic can’t be applied in situations involving common sense.
- If controllers can do the job perfectly without the use of fuzzy logic.
How is Fuzzy Logic different from conventional control methods?
FL integrates a simple, rule-based “IF X AND Y THEN Z”. The approach is to solve control problems digitally rather than mathematically. The module is empirically-based on the operator experience and not on their technical understanding.
In spite of dealing with temperature control in terms of 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 inaccurate and yet very descriptive of what must actually happen. Just assume what you will do if the temperature is too cold while taking shower: no doubt, you will make the water normal easily without any trouble. Fuzzy logic is capable of mimicking this type of behaviour but at a faster rate.
Fuzzy Logic Applications
These are some applications of fuzzy logic system.
a. Automotive Systems
- Automatic Gearboxes
- Four-Wheel Steering
- Vehicle environment control
b. Consumer Electronic Goods
- Hi-Fi Systems
- Still and Video Cameras
c. Domestic Goods
- Microwave Ovens
- Vacuum Cleaners
- Washing Machines
d. Environment Control
- Air Conditioners/Dryers/Heaters
Apart from these applications, fuzzy logic is used in various other sectors, have a look:
In Industries: The idea of fuzzy control is simply characterized by a control strategy expressed by a number of fuzzy control rules. Recent applications include water quality control, automatic train operating systems, elevator control, control of smart locomotives, cement kiln control, power electronics — speed control of DC motor, induction motor efficiency optimization control.
Products: Fuzzy logic has been introduced in consumer goods like washing machines and television sets. Recent work has been directed towards developing fuzzy logic-based handheld portable products.
Advantages of Fuzzy Logic System
- The Fuzzy logic system is very easy and understandable.
- The Fuzzy logic system is capable of providing the most effective solution to complex issues.
- The system can be modified easily to improve or alter the performance.
- The system helps in dealing engineering uncertainties.
- It is widely used for commercial and practical purposes.
- Fuzzy logic systems can be programmed in a situation when feedback sensors stop working.
- Economical sensor can be used which will help to keep overall system cost low.
- Robust setup as no precise inputs required.
- Fuzzy logic can be programmed in a situation where feedback sensor stops working.
Disadvantages of Fuzzy Logic Systems
- In fuzzy logic setting, exact rules and membership functions are difficult tasks.
- Fuzzy logic is not always correct, so the results are based on assumptions and may not be widely accepted.
- In some cases, fuzzy logic is confused with probability theory and terms.
- Extensive testing with hardware is required for validation and verification of fuzzy knowledge based systems.
- Fuzzy logic doesn’t have the capability of machine learning and neural network type pattern recognition.
In the near future, there is a tremendous scope of fuzzy logic-based products. One of the major areas where its use is needed to be investigated is the mobile communication system. Moreover, it also opens an opportunity for designing ASICs comprising integrated circuits for computational modules. In recent times, fuzzy logic has emerged as a powerful system in dealing with complex problems. Moreover, its role in handling uncertainty of complex engineering problems is also important. Generally, engineers and scientists face such complex problems which can’t be solved numerically through traditional mathematical methods, but by implementing fuzzy logic to the same, one can characterize and solve the complex problem.
Fuzzy logic has the capability to capture the inaccuracy of linguistic terms in statements. And, it can be concluded that fuzzy logic comes with greater capability to model human common-sense reasoning and decision making.
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