Fuzzy Logic

Fuzzy logic stems from the mathematical study of multivalued logic. Whereas ordinary logic deals with statements of absolute truth (such as, "Is this object green?"), fuzzy logic addresses sets with subjective or relative definitions, such as "tall," "large," or "beautiful." This attempts to mimic the way humans analyze problems and make decisions, in a way that relies on vague or imprecise values rather than absolute truth or falsehood. In practice, these constructs all allow for partial values of the "true" condition. Instead of requiring all statements to be absolutely true or absolutely false, as in classical logic, the truth values in fuzzy logic can be any value between zero and one. This creates an opportunity for algorithms to make decisions based on ranges of data as opposed to one discrete data point.

Fuzzy logic is frequently used in machine controllers and artificial intelligence and can also be applied to trading software. Although it has a wide range of applications, it also has substantial limitations. Because fuzzy logic mimics human decision-making, it is most useful for modeling complex problems with ambiguous or distorted inputs. Due to the similarities with natural language, fuzzy logic algorithms are easier to code than standard logical programming, and require fewer instructions, thereby saving on memory storage requirements.

Fuzzy Logic Advantages

• Fuzzy logic is more likely to reflect real-world problems than classical logic.
• Fuzzy logic algorithms have lower hardware requirements than classical boolean logic.
• Fuzzy algorithms can produce accurate results with imprecise or inaccurate data.

Fuzzy Logic Disadvantages

• Fuzzy algorithms require broad validation and verification.
• Fuzzy control systems are dependent on human expertise and knowledge.