Fuzzy Logic Explained: Real-Life Fuzzy Logic Applications

In computer science and human reasoning, fuzzy logic is a heuristic approach to decision-making that allows you to process multiple degrees of truth using the same variables. Using fuzzy rules and theories helps scientists better classify real-world truths, in which validations of an absolute true or false are rare.

Mathematician Lotfi Zadeh first introduced this system with the proposal of fuzzy set theory in his 1965 paper “Fuzzy Sets.” His theory showed people make decisions based on imprecise and nonnumerical information.

While classical logic concerns itself with absolute truths (such as, “Is the object red?”), fuzzy theory focuses more on relative definitions (such as, “Is the object short? Pretty? Warm?”).

In standard logic, scientists and mathematicians notate truth values with a 0 or 1. In fuzzy logic, however, data sets might reveal partial truths, such as 0.5 or 0.9. These questions allow for partial truths; in scientific practice, this will enable algorithms to base decisions on ranges of data instead of one singular data point.

Fuzzy logic applies several unique membership functions through a decision-tree type of analysis. A set of rule-based inferences (if-then rules) ascribes a degree of membership to a variable, which offers pattern recognition and forms a system of values and desired outputs.

IBM’s Watson computer, for example, relies on fuzzy semantics; the machine learned how to answer questions people pose in natural language. Each variable will have a unique membership value instead of a value representing a binary truth or falsehood. This makes the application of fuzzy logic’s rule base more dynamic, algebraic, and human-like for machinery.

The Mamdani system, which takes its name from the Tanzanian computer scientist Ebrahim H. Mamdani, is perhaps the most straightforward and popular fuzzy interference method. The three steps of the process are:

• Fuzzify the values. Fuzzify all input values to convert them into fuzzy membership functions.
• Compute the output functions. Apply all necessary rules to compute your fuzzy output functions.
• Defuzzify the fuzzy output functions. Defuzzify your output functions so you then have clean output values. Defuzzification is the creation of crisp values within your fuzzy logic controls and variables.

As a form of multivalued logic, fuzzy logic has various applications, including artificial intelligence, machine learning, and production processes. Fuzzy logic can help support subway system controls, like acceleration and deceleration, and assist handwriting recognition systems.

The New York Stock Exchange trading also uses fuzzy logic. In stock trading, the creator of the fuzzy logic system can create a subjective inference engine and control systems that automate specific trade signals according to specific criteria. Other real-world examples include seismological and meteorological institutes in Japan, which use fuzzy logic in the early detection of earthquakes.

Fuzzy logic mimics human decision-making, which can give it a set of advantages. This approach has the ability to:

1. 1. Function with lower hardware requirements: Soft computing works better with fuzzy logic, whereas classical logic requires more complex hardware.
2. 2. Produce accurate results with inaccurate data: Data fuzzification can still get accurate results as fuzzy logic’s pattern recognition and partial truths support reliable output analyses.
3. 3. Reflect real-life scenarios: When you’re trying to understand why people purchase specific consumer products or which stocks to buy at a particular time, having a fuzzy control and variable can create a system that provides logical outcomes.

Using fuzzy logic has its disadvantages. The approach is limiting in that it is:

1. 1. Incompatible with crisp values: If you seek a crisp set (0 or 1 truth values), it would be better to rely on other expert systems, such as Boolean logic.
2. 2. Requires broad verifications: By nature, the validations in fuzzy logic will be more qualitative and subjective, making them more open to interpretation.
3. 3. Still susceptible to human error: Fuzzy logic systems rely on human knowledge and expertise, limiting their scope and analyses.

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