What is fuzzy logic?
Fuzzy logic is an approach to variable processing that allows multiple possible truth values to be processed through the same variable. Fuzzy logic attempts to solve problems with an open and imprecise spectrum of data and heuristic which allows for a range of specific conclusions.
Fuzzy logic is designed to solve problems by considering all available information and making the best possible decision given the input.
Key points to remember
- Fuzzy logic is a heuristic approach that allows for more advanced decision tree processing and better integration with rule-based programming.
- Fuzzy logic is a generalization of standard logic, in which all statements have a truth value of one or zero. In fuzzy logic, statements can have a partial truth value, such as 0.9 or 0.5.
- Theoretically, this gives the approach more opportunities to mimic real-life circumstances, where statements of absolute truth or untruth are rare.
- Fuzzy logic can be used by quantitative analysts to improve the execution of their algorithms.
- Due to similarities to ordinary language, fuzzy algorithms are relatively simple to code, but they may require extensive verification and testing.
Understand fuzzy logic
Fuzzy logic stems from the mathematical study of many-valued logic. While ordinary logic deals with statements of absolute truth (such as “Is this object green?”), fuzzy logic deals with sets with subjective or relative definitions, such as “great”, “great” or “beautiful “. It 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 partial values of the “true” condition. Instead of requiring all statements to be absolutely true or absolutely false, as in classical logic, 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 a discrete data point.
Today, fuzzy logic is used in a wide range of applications including: aerospace engineering, automotive traffic control, business decision making, industrial processes, artificial intelligence, and machine learning.
In standard logic, each instruction must have an absolute value: true or false. In fuzzy logic, truth values are replaced by “membership” degrees from 0 to 1, where 1 is absolutely true and 0 is absolutely false.
History of fuzzy logic
Fuzzy logic was first proposed by Lotfi Zadeh in a 1965 article for the journal Information and Control. In his paper, titled “Fuzzy Sets”, Zadeh attempted to reflect the type of data used in information processing and derived the elementary logical rules for this type of set.
“Most often, classes of objects encountered in the real physical world do not have precisely defined membership criteria,” Zadeh explained. “Yet the fact remains that such loosely defined ‘classes’ play an important role in human thought, especially in the areas of pattern recognition, information communication, and abstraction.”
Since then, fuzzy logic has been successfully applied in machine control systems, image processing, artificial intelligence, and other fields that rely on ambiguously interpreted signals.
Fuzzy logic and decision trees
Fuzzy logic in its most basic sense is developed through decision tree type analysis. Thus, on a larger scale, it forms the basis of artificial intelligence systems programmed by rule-based inferences.
Generally, the term fuzzy refers to the large number of scenarios that can be developed in a decision tree type system. The development of fuzzy logic protocols may require the integration of rule-based programming. These programming rules can be qualified as fuzzy sets because they are elaborated according to comprehensive models.
Fuzzy sets can also be more complex. In more complex programming analogies, programmers may have the ability to expand the rules used to determine the inclusion and exclusion of variables. This can result in a wider range of options with less precise rule-based reasoning.
Fuzzy logic can be used in trading software, where it is used to analyze market data for buy and sell signals.
Fuzzy semantics in artificial intelligence
The concept of fuzzy logic and fuzzy semantics is central to the programming of artificial intelligence solutions. Artificial intelligence solutions and tools continue to grow in the economy in various sectors as fuzzy logic programming capabilities also grow.
IBM’s Watson is one of the best-known artificial intelligence systems using variations of fuzzy logic and fuzzy semantics. Specifically in financial services, fuzzy logic is used in machine learning and technology systems supporting investment intelligence results.
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 wide range of changing market variables that affect their investments.
Examples of fuzzy logic
In advanced software trading models, systems can use programmable fuzzy sets to analyze thousands of stocks in real time and present the investor with the best opportunity available. Fuzzy logic is often used when a Trader seeks to use multiple factors to consider. This may result in restricted analysis of trading decisions. Merchants may also have the ability to program a variety of rules for implementing trades. Here are two examples:
- Rule 1: If the moving average is low and the Relative Strength Index (RSI) is low, then sell.
- Rule 2: If the moving average is high and the relative strength index (RSI) is high, so buy.
Fuzzy logic allows a trader to program their own subjective low and high inferences into these basic examples to arrive at their own automated trading signals.
Advantages and disadvantages of fuzzy logic
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 significant limitations.
Since fuzzy logic mimics human decision-making, it is especially useful for modeling complex problems with ambiguous or distorted inputs. Due to similarities to natural language, fuzzy logic algorithms are easier to code than standard logic programming and require fewer instructions, saving on memory storage requirements.
These benefits also come with drawbacks, due to the imprecise nature of fuzzy logic. Since systems are designed for inaccurate data and input, they must be tested and validated to avoid inaccurate results.
Advantages and disadvantages of fuzzy logic
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Fuzzy logic is more likely to reflect real-world problems than classical logic.
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Fuzzy logic algorithms have lower hardware requirements than classical Boolean logic.
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Fuzzy algorithms can produce accurate results with imprecise or inaccurate data.
What is fuzzy logic in data mining?
Data mining is the process of identifying meaningful relationships in large data sets, a field that straddles statistics, machine learning, and computer science. Fuzzy logic is a set of rules that can be used to draw logical conclusions from fuzzy sets of data. Since data mining is often applied to imprecise measurements, fuzzy logic is a useful way to determine relevant relationships from this type of data.
Is fuzzy logic the same as machine learning?
Fuzzy logic is often lumped together with machine learning, but it’s not the same thing. Machine learning refers to computer systems that mimic human cognition, iteratively adapting algorithms to solve complex problems. Fuzzy logic is a set of rules and functions that can work on imprecise data sets, but the algorithms still need to be coded by humans. Both fields have applications in artificial intelligence and in solving complex problems.
What is the difference between fuzzy logic and neural networks?
an artificial neural network is a computer system designed to mimic the problem-solving procedures of a human-like nervous system. This is distinct from fuzzy logic, a set of rules designed to draw conclusions from imprecise data. Both have applications in computing, but they are separate fields.
What are the components of fuzzy logic?
Fuzzy logic is often described as having four components:
- fuzzification. The process of converting specific input values to a certain degree of membership in fuzzy sets based on their suitability.
- Fuzzy rules / knowledge base. These are the If-Then rules to follow, often derived from expert opinion or via more quantitative approaches.
- Inference method. The way to obtain the final fuzzy conclusion, according to the degree of membership of the input variables to the fuzzy sets and the detailed fuzzy rules
- Defuzzification. Process of converting fuzzy conclusions into detailed output values.
The essential
Fuzzy logic is an extension of classical logic that incorporates the uncertainties that influence human decision-making. It is frequently used to solve complex problems, where parameters may be unclear or imprecise. Fuzzy logic is also used in investment software, where it can be used to interpret ambiguous or unclear trading signals.