11-02-2013, 11:52 AM
AUTOMIZATION OF UMPIRING IN CRICKET USING FUZZY LOGIC
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ABSTRACT
Fuzzy logic has been introduced to deal with vague, imprecise and uncertain problems. A fuzzy logic controller can be regarded as an expert system that is able to process qualitative variables and to infer crisp values out of uncertainty. Hence, fuzzy logic can find applications in many aspects of real life, where there is lack of information, there is uncertainty. A good example of such an application is in AUTOMIZATION OF UMPIRING IN CRICKET
Cricket now- a -days has gone beyond the scope of a ‘game’ to be a inherent part of our senses. While millions of money is invested in this sport there exist still some discrepancies that hit heavily on the reverence to the game. Humans are always prone to errors, which hold well even with the umpires. Thus automization of the decisions will help to improve the essence of the game and ensure an even more cheerful and judicious entertainment.
INTRODUCTION
FUZZY LOGIC
Fuzzy logic was introduced by Prof. Lofti Zadeh in 1965 as a mathematical way to represent uncertainty in everyday life. It can provide the means to represent vague and fuzzy information, manipulate it, and to draw inferences from it. In ordinary mathematics, information is of a crisp kind. It belongs to a set or it does not. The choice of a yes-or-no answer is possible and usually applied, but information could be lost in such a choice, as the degree of belonging is not taken into consideration. A fuzzy model is the idea of a fuzzy set. A fuzzy set differs from conventional (crisp) sets in its semi-permeable boundary membrane. Instead of a characteristic function that has 2 states, inclusion (1) or exclusion (2), the fuzzy set has a function that admits a degree of membership in the set from complete exclusion (0) to absolute inclusion (1). The value zero is used to symbolize complete non-membership, the value 1 is used to symbolize complete membership, and values in between are used to symbolize intermediate degrees of membership. Membership in a fuzzy subset should not be on a 0 or 1 basis, but rather on a 0 to 1 scale; that is the membership should be an element of the interval [0,1]. A fuzzy concept is a linguistic variable used to define a fuzzy subset, as CLOSE or FAR for a range of obstacle. A Fuzzy set comes as a generalization of conventional set theory.
COMPONENTS OF FUZZY LOGIC DECISION-MAKING SYSTEM
The four principal components of the fuzzy decision-making systems are:
1. The fuzzifier determines input and output variables and maps them into linguistic variables that are to be displayed on a universe of discourse.
2. The rule base: is a part of expert systems that contains the domain knowledge. Membership functions and control rules are decided by the experts at this point, based on their knowledge of the system.