In real life, decision-making often involves uncertainty and variability that is not easily handled by traditional binary logical systems. Fuzzy logic, which was first introduced by Lotfi A scientist Zadeh in 1965, was a mathematical approach that allowed us to deal with uncertainty and ambition in the process of decision making. By combining the basic concepts of mathematics and the theory of society, fuzzy logic paves the way for more flexible and adaptive systems in understanding complex and ambiguous problems. This article will discuss the basic concept of fuzzy logic, its role in decision-making, and its application in various industrial areas.

1. Basic Understanding Logic Fuzzy

Fuzzy logic is a development of classic theory of holiness and binary logic. In binary logic, any statement or object is considered entirely true (1) or entirely wrong (0). Instead, fuzzy logic allows a value to have a degree of varied truth, represented by numbers between 0 and 1.

For example, in binary logic, the temperature "warm" may not be represented precisely because it's too ambiguous to categorize "hot" or "cold" completely. In fuzzy logic, warm temperatures can have certain membership values within range 0 to 1, which describes how close that temperature is to "heat" or "cold."

2. Mathematical Base in Fuzzy Logic

Basically, fuzzy logic involves a fuzzy set of sets used to describe the conditions or characteristics of which are ambiguous. Each element in this set has a membership value that determines the extent of which it is qualified to be a member of the union. This membership function, which is often a Gaussian curve graph or triangle, is used to calculate values between 0 and 1 for a particular variable.

For example, if we define the fuzzy set "high" for human heights, a person with a 180 cm height might have a 0.8 membership value in the "high" and 0.2 set of "medium." These values allow fuzzy logic to deal with uncertainty and ambition in complex situations.

3. Operator in Fuzzy Logic

Some operators used in fuzzy logic to combine membership values from various fuzzy sets among others:

• AND operator: Taking minimum value of two degrees membership.
• OR operator: Taking maximum value of two degrees membership.
• Operator NOT: Retrieving complementary value, or 1 minus membership value.

With this operator, fuzzy logic can make decisions more flexible than with traditional binary systems.

Four. Take a verdict with Fuzzy Logic

Fuzzy logic is very useful in decision-making because it can handle difficult variable-variable certified precision. Fuzzy systems usually consist of three main components:

1. Fuzzification: Process where numeric input variables are converted into fuzzy membership values.
2. Fuzzy Inference Engine: Process fuzzy rules rules to generate output.
3. Defuzzification: Convert fuzzy output to numerical value that can be used for decision making.

By using fuzzy logic, decision makers can establish fuzzy rules based on human knowledge and experience. For example, in the case of the auto-control system on the car, the fuzzy rule can be made like, "If it's high speed and immediate distance, then reduce speed drastically."

5. Fuzzy Logic Applied in Various Field

The fuzzy logic has extensive applications in various fields, among which:

• Automatic Control: Fuzzy logic is used in a control system involving sustainable decision-making, like air conditioning and temperature control systems, where conditions cannot be measured properly. By fuzzy logic, the system can set temperature and humidity conditions based on the fuzzy rules that have been set.
• Weather ServicesIn meteorology, fuzzy logic is used to process weather data that has high uncertainty. By using fuzzy algorithms, the weather system can provide a more flexible estimate.
• Financial and EconomicFuzzy logic is often used in risk modeling, investment analysis, and stock market predictions, where high uncertainty factors and market conditions are hard to predict accurately.
• Medical Decision Supporter SystemFuzzy logic is used to create a decision-supporting system that helps doctors diagnose diseases based on uncertain or inmeasurable symptoms.

Six. The excellence of Fuzzy Logic in Decisions

Fuzzy logic provides a number of benefits in decision-making that involve uncertainty, among other things:

• Flexibility: Fuzzy logic can handle uncertain or ambiguous information, which cannot be processed with ordinary binary logic.
• Easy to adaptFuzzy logic can be adjusted to human knowledge and intuition through fuzzy setting rules.
• Vast application: With flexible characteristics, fuzzy logic can be applied in various fields that require adaptive and fast decision-making.

Seven. The challenge and limitations of Fuzzy Logic

Although it has many advantages, fuzzy logic also has challenges and limitations, such as:

• Complex in Membership Function Enforcement: An accurate membership function requires a deep understanding of the system to be controlled.
• The difficulty in enlisting the rulesFuzzy logic requires precise and precise rules, which are often difficult to determine, especially in complex systems.
• Not Always Accurate:

Eight. Future of Fuzzy Logic in Technology

As technology progresses more and more sophisticated, fuzzy logic is expected to continue to play an important role in decision-making, especially in AI and automation. In the AI world, fuzzy logic can be used along with machine learning methods to increase the accuracy and effectiveness of the AI system in handling ambiguous data.

Moreover, with applications of technology such as Lot and big data, fuzzy logic has great potential to apply to systems that require reality-time analysis and data-based decision making.

Conclusion

Fuzzy logic is a revolutionary mathematical concept of decision-making uncertainty. With the flexibility of dealing with uncertain and ambiguous data, fuzzy logic allows us to create a system that is more adaptive and responsive in different fields such as automatic control, weather, financial and health. Although fuzzy logic has a challenge in terms of determining the function of membership and rules, it has the ability to deal with ambiguous information, making it a valuable tool in the age of modern technology and digitalization all-time.

Source: Zahah, L.A. (1965). Fuzzy Sets.