Fuzzy learning
Fuzzy logic is considered to be a set of mathematical principles of knowledge representation based on degree of membership. It deals with reasoning that is approximate rather than fixed and exact in comparison to traditional binary sets. The fuzzy logic variables may have truth value that ranges in degree between 0 and 1. Fuzzy logic has been successfully applied in the process of controlling, estimation, modelling, identification, stock market prediction problems like these in the real world quite often turn out to be complex. Owing to an element uncertainty. There is such a situation where fuzzy logic exhibits immense potential for effective solving of uncertainty in the problem. There are basically three steps involved in fuzzy logic and they are fuzzification, inference and defuzzification. Fuzzification is the process of making crisp quantity fuzzy. We do this by simply recognizing that many of the quantities we consider to be crisp and are not actually deterministic at all. They carry considerable uncertainty. The core section of fuzzy system is that it combines the fact obtained from the fuzzification with the rule based and fuzzy reasoning process. This is called fuzzy machine. Defuzzification is the process of producing a quantifiable result in fuzzy logic from fuzzy sets and corresponding membership degree. It is typically needed in fuzzy control system. Defuzzification is the interpretation of the membership degree from the fuzzy sets.
Summary
Fuzzy logic is considered to be a set of mathematical principles of knowledge representation based on degree of membership. It deals with reasoning that is approximate rather than fixed and exact in comparison to traditional binary sets. The fuzzy logic variables may have truth value that ranges in degree between 0 and 1. Fuzzy logic has been successfully applied in the process of controlling, estimation, modelling, identification, stock market prediction problems like these in the real world quite often turn out to be complex. Owing to an element uncertainty. There is such a situation where fuzzy logic exhibits immense potential for effective solving of uncertainty in the problem. There are basically three steps involved in fuzzy logic and they are fuzzification, inference and defuzzification. Fuzzification is the process of making crisp quantity fuzzy. We do this by simply recognizing that many of the quantities we consider to be crisp and are not actually deterministic at all. They carry considerable uncertainty. The core section of fuzzy system is that it combines the fact obtained from the fuzzification with the rule based and fuzzy reasoning process. This is called fuzzy machine. Defuzzification is the process of producing a quantifiable result in fuzzy logic from fuzzy sets and corresponding membership degree. It is typically needed in fuzzy control system. Defuzzification is the interpretation of the membership degree from the fuzzy sets.
Things to Remember
- Fuzzy logic is considered to be a set of mathematical principles of knowledge representation based on degree of membership. It deals with reasoning that is approximate rather than fixed and exact in comparison to traditional binary sets.
- The fuzzy logic variables may have truth value that ranges in degree between 0 and 1.
- Fuzzy logic has been successfully applied in the process of controlling, estimation, modelling, identification, stock market prediction problems like these in the real world quite often turn out to be complex.
- There is such a situation where fuzzy logic exhibits immense potential for effective solving of uncertainty in the problem.
- There are basically three steps involved in fuzzy logic and they are fuzzification, inference and defuzzification.
- Fuzzification is the process of making crisp quantity fuzzy. We do this by simply recognizing that many of the quantities we consider to be crisp and are not actually deterministic at all. They carry considerable uncertainty.
- The core section of fuzzy system is that it combines the fact obtained from the fuzzification with the rule based and fuzzy reasoning process. This is called fuzzy machine.
- Defuzzification is the process of producing a quantifiable result in fuzzy logic from fuzzy sets and corresponding membership degree. It is typically needed in fuzzy control system. Defuzzification is the interpretation of the membership degree from the fuzzy sets.
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Fuzzy learning
Fuzzy logic:
Fuzzy logic is considered to be a set of mathematical principles of knowledge representation based on degree of membership. It deals with reasoning that is approximate rather than fixed and exact in comparison to traditional binary sets. The fuzzy logic variables may have truth value that ranges in degree between 0 and 1. Fuzzy logic has been extended to handle the concept of partial truth where the truth value may range between completely true and completely false.
It has been argued that the human-thinking does not always follow crisp "Yes/No" logic which is often vague, uncertain and fuzzy in nature. Based on the nature of human thinking, Loffizadeh devised a new theory which is away from the Boolean logic known as fuzzy logic. Fuzzy logic has been successfully applied in the process of controlling, estimation, modelling, identification, stock market prediction problems like these in the real world quite often turn out to be complex. Owing to an element uncertainty. There is such a situation where fuzzy logic exhibits immense potential for effective solving of uncertainty in the problem.
There are basically three steps involved in fuzzy logic. Following are the steps:
- Fuzzzification: It is the process of making crisp quantity fuzzy. We do this by simply recognizing that many of the quantities we consider to be crisp and are not actually deterministic at all. They carry considerable uncertainty. If the form of uncertainty arises because of vagueness then the variable is probably fuzzy and can be represented by the member of function.
- Inference: The core section of fuzzy system is that it combines the fact obtained from the fuzzification with the rule based and fuzzy reasoning process. This is called fuzzy machine.
- Defuzzification: It is the process of producing a quantifiable result in fuzzy logic from fuzzy sets and corresponding membership degree. It is typically needed in fuzzy control system. Defuzzification is the interpretation of the membership degree from the fuzzy sets.
References:
- Elaine Rich, Kevin Knight 1991, "Artificial Intelligence".
- Nilsson, Nils J. Principles of Artificial Intelligence, Narosa Publishing House New Delhi, 1998.
- Norvig, Peter & Russel, Stuart Artificial Intelligence: A modern Approach, Prentice Hall, NJ, 1995
- Patterson, Dan W. Introduction to Artificial Intelligence and Expert Systems, Prentice Hall of India Private Limited New Delhi, 1998.
Lesson
Machine Learning
Subject
Computer Engineering
Grade
Engineering
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