14-07-2012, 04:51 PM
AUTOMATION OF UMPIRING IN CRICKET USING FUZZY LOGIC
[attachment=27698]
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. This paper puts forward a proposal, which aims at achieving the following objectives
The main objectives we propose to solve are:
o Unravel the contradiction between a boundary and a six.
o Identify an LBW.
o Identify a catch.
The tool we have used is “Fuzzy Logic” as it is a good decision maker.
Neural networks can be trained using real time data which makes it highly efficient in operation.
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. It is a superset of conventional (crisp) logic that has been extended to handle the concept of partial truth (truth values between “completely true” and “completely false”); allowing intermediate values between crisp values.
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.
WHY USE “FUZZY LOGIC” AS A TOOL?
Fuzzy Logic methodology, a branch of Artificial Intelligence is basically characterized by three traits.
First, it does not consider whether something is true or false, but rather how true the statement is.
Second, because it is similar to human reasoning, its implementation tends to be based on natural language.
The third trait is that it is flexible and can model a complex, non-linear system by using imprecise information.
The mode that has just been described provides an immediate output which makes the fuzzy as the choicest tool for our problem.
[attachment=27698]
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. This paper puts forward a proposal, which aims at achieving the following objectives
The main objectives we propose to solve are:
o Unravel the contradiction between a boundary and a six.
o Identify an LBW.
o Identify a catch.
The tool we have used is “Fuzzy Logic” as it is a good decision maker.
Neural networks can be trained using real time data which makes it highly efficient in operation.
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. It is a superset of conventional (crisp) logic that has been extended to handle the concept of partial truth (truth values between “completely true” and “completely false”); allowing intermediate values between crisp values.
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.
WHY USE “FUZZY LOGIC” AS A TOOL?
Fuzzy Logic methodology, a branch of Artificial Intelligence is basically characterized by three traits.
First, it does not consider whether something is true or false, but rather how true the statement is.
Second, because it is similar to human reasoning, its implementation tends to be based on natural language.
The third trait is that it is flexible and can model a complex, non-linear system by using imprecise information.
The mode that has just been described provides an immediate output which makes the fuzzy as the choicest tool for our problem.