What are fuzzy expert systems?

Fuzzy Expert System is a form of solving problems used by a computer system, often used in creating artificial intelligence. Professional systems are types of computer software to decide based on logic Boole, which means that the system uses a number of answers yes or no to try to solve the problem. Fuzzy Expert Systems expand about the traditional expert system and are based on fuzzy logic instead of Boolean logic. Fuzzy logic, as the name suggests, means that the answer is not clear yes or no. It falls somewhere in the middle and the computer must try to calculate the response on the basis of responses that may not be completely true, but may not be completely false either.

known as "Father Fuzzy Logic", Dr. Lotfi Zadeh introduced the concept of fuzzy logic at the age of 60 while employed at the University of California in Berkeley. In 1965 he published a document that includes Fuzzy Sady. He explained not only the idea of ​​Fuzza Sad and Logic, but also a framework for incorporating this new hornbeam into the world of engineersStub. He also created the term "fuzzy", referring to this particular logical style and the name stuck.

In order to understand the theory for Fuzzy Expert Systems, it is necessary to understand the basic concepts of Boolean logic and fuzzy logic. Although both rely on advanced mathematical algorithms, the basic concept is simple. Both use answers to a number of questions or statements to formulate a new answer. In Boolean logic, the answers are either true or false, while in Fuzzy Logic, the answer may be true, partially true, false, partially false and several values ​​between them, depending on what programmer's inputs to the program.

For example, if the professional system wanted to decide using Boolean logic, he would eventually answer truthfully or false, also referred to as yes or no. However, a professional system using fuzzy logic could answer yes, no, maybe or other whoeverbinis. It does this by drawing conclusions from its current knowledge base of information.

Knowledge bases are the heart of fuzzy expert systems. If the computer cannot come up with the correct answer, it is assumed that the knowledge base does not contain enough information, rather than provided the program itself is bad. The knowledge base may contain command like "when x = yes and y = not then z = maybe." From this statement, Fuzzy Expert systems conclude that when "x = yes" and "y = yes", "Z" must also equal "yes", or that "x = no" and "y = yes" that "Z" is still "possible". If this is not the answer that the programmer wanted, it means that the knowledge base needs more information to come up with the right answer.

Fuzzy Expert Systems creates these calculations on the basis of mathematical values. "Yes," "No" and "maybe" are assigned certain values. The computer deals with what values ​​of terms in commands, such as "x = yes and y = no", are equal and adding values. Then adds jaany other relevant values ​​and corresponds to the final value with the response such as "possible", "yes" or "no". So adding mathematical values ​​"x = no" and "y = yes" tells the computer that the mathematical value for "z" is equal to "maybe."