Application of Fuzzy Inference Systems in Real World Scenarios

Abstract

The use of computing technologies by humans for various knowledge processing and knowledge synthesis activities has grown exponentially in the recent past. One of the obvious reasons for this trend is the ease with which information is obtained, processed and computed to obtain the desired analysis. Some real-world examples of such tasks include control of a train (for example on the Sendai Subway System) [1], control of heating and cooling devices [2], signal processing [3], controlling different functions of an aircraft [4] etc. At the same time, there has also been a change in the type of users who depend on such computing technologies for information. Users range from the experienced to the intermediate or novice group of users, who are not interested in knowing the complex computations by which information is processed, but want the desired output from the computations in order to carry out various tasks. For such tasks, it is of critical importance that decision making by machines be done with the utmost precision based on an understanding of the current input scenarios. In other words, machines have to mimic the real-time decision making capability of humans so as to conduct an intelligent analysis of the underlying complex information, synthesize knowledge from it, and act on it for various activities.