The term fuzzy logic was coined in 1965 by Lotfi Zadeh, University of California. He observed that conventional computer logic could not manipulate data representing subjective or ambiguous human concepts.

From control theory to artificial intelligence, fuzzy algorithms have been utilized in numerous disciplines. It was designed to enable the computer to distinguish between true and false data. Something comparable to the human cognitive process. Similar to Some dimness, Some luminosity, etc.

Possibilities

The conventional logic block a computer understands requires precise input and generates a definite output as TRUE or FALSE, corresponding to a human's YES or NO. Lotfi Zadeh observed that, unlike computers, humans have a range of possibilities between YES and NO, including:

• Certainly Yes
• Possibly Yes
• Cannot Say
• Possibly No
• Certainly No

Architecture

The fuzzy Architecture is made up of four parts:

• BASE RULE: It includes a collection of rules and IF-THEN criteria established by specialists to manage the decision-making mechanism based on linguistic data. Recent advances in fuzzy theory provide various viable strategies for designing and tuning fuzzy controllers. The majority of these advancements lower the number of fuzzy rules.
• FUZZIFICATION: It transforms inputs like crisp numbers into fuzzy sets. Crisp inputs are the precise inputs measured by sensors and supplied into the control system for processing, such as temperature, pressure, rpm, etc. This component divides the input signals into the following five states in any Fuzzy Logic system:
• Large Positive (LP)
• Medium Positive (MP)
• Small (S)
• Medium Negative (MN)
• Large negative (LN)
• INFERENCE ENGINE: It determines the degree of matching of the current fuzzy input about each rule and selects which rules should be fired based on the input field. The control actions are then formed by combining the fired rules.
• DEFUZZIFICATION: It turns the inference engine's fuzzy sets into a crisp value. 

Advantages

• The application of fuzzy logic in data mining facilitates the management of uncertainty encountered in engineering contexts.
• The system is highly robust as it does not rely on specific inputs.
• The programming can accommodate situations in which the feedback sensor becomes non-functional.
• The system's performance can be readily enhanced or adjusted.
• Utilizing cost-effective sensors mitigates the overall expenses and intricacy of the system.
• It offers a highly efficient resolution to intricate problems.

Applications

• It is utilized in the aerospace industry for spacecraft and satellite altitude regulation.
• It has been utilized for pace control and traffic control in automotive systems.
• In the business of large companies, it is used for decision-making support systems and personal evaluation.
• It has applications in the chemical industry for pH regulation, dehydration, and chemical distillation.
• In Natural language processing and numerous Artificial Intelligence applications, fuzzy logic is utilized.
• Modern control systems, such as expert systems, use fuzzy logic extensively.
• Fuzzy Logic is utilized with Neural Networks because it replicates the decision-making process of a human but much more quickly. It is achieved by aggregating data and transforming it into more meaningful data by generating fuzzy sets of partial truths.

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