Fuzzy Logic
Fuzzy Logic is a variable processing method that allows multiple possible truth values to be processed by the same variable. Fuzzy logic attempts to solve problems through an open, imprecise data spectrum and heuristic methods to obtain a series of accurate conclusions.
Fuzzy logic aims to solve problems by taking into account all available information and making the best decision based on the input.
History of Fuzzy Logic
Fuzzy logic was first proposed by Lotfi Zadeh in a paper published in the journal Information and Control in 1965..In his paper titled "Fuzzy Sets", Zadeh attempted to reflect the types of data used in information processing and to derive the basic logical rules for such sets.
"More often than not, categories of objects encountered in the real physical world do not have precisely defined membership criteria," Zadeh explained. "Yet the fact remains that such imprecisely defined 'categories' play an important role in human thinking, particularly in the areas of pattern recognition, information transfer, and abstraction."
Since then, fuzzy logic has been successfully applied to machine control systems, image processing, artificial intelligence, and other fields that rely on signals with fuzzy interpretations.
Fuzzy Logic and Decision Trees
In its most basic sense, fuzzy logic was developed through decision tree type analysis. Therefore, in a wider context, it forms the basis of artificial intelligence systems based on rule-based reasoning.
In general, the term "fuzzy" refers to the large number of scenarios that can be developed in a decision tree-like system. Developing fuzzy logic protocols may require the integration of rule-based programming. These programming rules can be called fuzzy sets because they are developed on their own based on a comprehensive model.
Fuzzy sets can also be more complex. In a more complex programming analogy, a programmer may have the ability to expand the rules used to determine the inclusion and exclusion of variables. This can lead to a wider range of options with less precise rule-based reasoning.
Fuzzy semantics in artificial intelligence
The concepts of fuzzy logic and fuzzy semantics are core components of programming AI solutions. As the programming capabilities of fuzzy logic continue to expand, AI solutions and tools continue to expand in various sectors of the economy.
One of the most well-known AI systems is IBM’s Watson, which uses variations of fuzzy logic and fuzzy semantics. Particularly in the financial services sector, fuzzy logic is used in machine learning and technical systems that support investment intelligence outputs.
In some advanced trading models, the integration of fuzzy logic mathematics can also be used to help analysts create automated buy and sell signals. These systems help investors react to a variety of changing market variables that affect their investments.
Advantages and Disadvantages of Fuzzy Logic
advantage
- Fuzzy logic reflects real-world problems better 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.
shortcoming
- Fuzzy algorithms require extensive verification and validation.
- Fuzzy control systems rely on human expertise and knowledge.