What are experts?
technology has always been about better, faster and smarter machines. Professional systems accept this concept using advanced computer logic to create a software that seems to be "thinking" and deciding on its own. Traditionally based on Boolean logic - logic using only real or false values - professional systems use complex algorithms to calculate answers from a large database of information. If the computer cannot determine the correct answer, the program is assumed that the program is incorrect, but that the knowledge base does not contain enough information about the subject.
When a computer has to make a decision, everything is divided into a number of real or false statements. If it is programmed to light up when the button is pressed, press it to TRUE and presses the button to set the FALSE. FALSE means no light while true turns on light. This is the basis of computer logic.
The professional system takes these real and false answers to a new level. SublEcombination of a number of real and false answers is trying to determine how to respond to a certain situation. It can change its response based on a particular pattern and the number of actual and false answers.
The idea of these systems is based on how people think. People can store a huge amount of new knowledge and make decisions based on previous knowledge. The computer is programmed to “think” and make the decision based on the knowledge found in its database and in previous experience. It is as if the computer “learns” from their past successes and failures.
There are two main forms of professional systems. The traditional expert system uses boolean logic to make decisions. On the other hand, not. It calculates a number of values that fall among the simple true or false answers to find out to what extent the statement is more true or more false.
Fuzzy Expert Systems are more human than traditional expert systems in the way they think. These professional systems are not given specific answers to the problem, but rather due to one statement from which they draw further conclusions. This process is known as an inference.
For example, if the statement is: "All female cats are striped. Miss Kitty is a female cat," the Fuzzy Expert Systems would cause that all female cats are striped and Miss Kitty is a woman, Miss Kitty must be striped. Fuzzy logic can also calculate more complicated values, such as determining the likelihood that a particular female cat will be striped if it has only a percentage of female cats. Traditional expert systems would need much more instructions to achieve the same conclusions.