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Reseach Article

Fuzzy Logic based Cricket Player Performance Evaluator

Published on None 2011 by Gursharan Singh, Nitin Bhatia, Sawtantar Singh
Artificial Intelligence Techniques - Novel Approaches & Practical Applications
Foundation of Computer Science USA
AIT - Number 1
None 2011
Authors: Gursharan Singh, Nitin Bhatia, Sawtantar Singh
337d1049-a371-4473-9963-a5d6dfaacb4c

Gursharan Singh, Nitin Bhatia, Sawtantar Singh . Fuzzy Logic based Cricket Player Performance Evaluator. Artificial Intelligence Techniques - Novel Approaches & Practical Applications. AIT, 1 (None 2011), 11-16.

@article{
author = { Gursharan Singh, Nitin Bhatia, Sawtantar Singh },
title = { Fuzzy Logic based Cricket Player Performance Evaluator },
journal = { Artificial Intelligence Techniques - Novel Approaches & Practical Applications },
issue_date = { None 2011 },
volume = { AIT },
number = { 1 },
month = { None },
year = { 2011 },
issn = 0975-8887,
pages = { 11-16 },
numpages = 6,
url = { /specialissues/ait/number1/2825-206/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Special Issue Article
%1 Artificial Intelligence Techniques - Novel Approaches & Practical Applications
%A Gursharan Singh
%A Nitin Bhatia
%A Sawtantar Singh
%T Fuzzy Logic based Cricket Player Performance Evaluator
%J Artificial Intelligence Techniques - Novel Approaches & Practical Applications
%@ 0975-8887
%V AIT
%N 1
%P 11-16
%D 2011
%I International Journal of Computer Applications
Abstract

Cricket is amongst the most popular sports. Performance of players directly affects their ranking internationally. We propose a fuzzy logic based technique to evaluate the performance of cricket players. Various input parameters are being considered which are scaled using linguistic variables and a very simple yet effective software tool is developed to compute the effect of input parameters on the ranking of the players.

References
  1. Mendel, J. M. 1995. Fuzzy logic systems for engineering: a tutorial. In Proceedings of the IEEE. Vol. 83. No. 3, March 1995.
  2. Zadeh, L. A. 1965. Fuzzy sets. Information and Control. 8, (1965) 338-353.
  3. Homaifar, A. and McCormick, E. 1995. Simultaneous design of membership functions and rule sets for fuzzy controllers using genetic algorithms. IEEE Trans. Fuzzy Systems. 3 (2), (May 1995).
  4. Yao, Y. Y. 1996. Two views of the theory of rough sets infinite universes. International Journal of Approximation Reasoning. 15, (1996) 291-317.
  5. Yao, Y. Y. 1998. A comparative study of fuzzy sets and rough sets. Information Sciences. 109 (1-4), (1998) 227-242.
  6. Hayward, G. and Davidson, V. 2003. Fuzzy logic applications. Analyst. 128, (2003) 1304–1306.
  7. Wang, W. and Bridges, S. M. 2000. Genetic algorithm optimization of membership functions for mining fuzzy association rules. In Proceedings of The International Joint Conference on Information Systems, Fuzzy Theory and Technology Conference, (March 2, 2000).
  8. Abraham, A. 2005. Adaptation of fuzzy inference system using neural learning. Fuzzy System Engineering: Theory and Practice. N. Nedjah, Ed. et al. Berlin, Germany: Springer-Verlag, 3, (2005) 53–83.
  9. Shafiq, M. Z., Farooq, M. and Khayam, S. A. 2008. A comparative study of fuzzy inference systems. Neural Networks and Adaptive Neuro Fuzzy Inference Systems for Portscan Detection. EvoCOMNET, LNCS, (2008).
  10. Zhiyi, F. 2004. A fuzzy inference system for synthetic evaluation of compost maturity and stability. Masters of Engineering thesis, University of Regina, Saskatchewan. (March 2004).
  11. Kumar, S., Bhatia, N. and Kapoor, N. 2011. Fuzzy logic based tool for loan risk prediction. In Proceedings of International Conference on Communication and Computing Technologies (ICCCT-2011), (Feb 25-26, 2011), 180-183.
  12. Kumar, S., Bhatia, N. and Kapoor, N. 2011. Software risk analysis using fuzzy logic. International Journal of Computer Information Systems, 2 (2), (2011), 7-12.
  13. Shaout, A., King, B. and Reisner, L. 2006. Real time game design of pac-man using fuzzy logic. The International Arab Journal of Information Technology. 3 (4), (October 2006).
  14. Curtis, K. M. 2010. Cricket batting technique analyser/trainer: a proposed solution using fuzzy set theory to assist West Indies cricket. In Proceedings of the 9th WSEAS international conference on Artificial intelligence, knowledge engineering and data base. (2010) 71-76.
  15. Chua, S. C., Tan, W. C., Wong, E. K. and Koo, V. C. 2002. Decision algorithm for pool using fuzzy system. Artificial Intelligence in Engineering & Technology, (2002) 370–375.
  16. Riley, J. 2005. Evolving fuzzy rules for goal-scoring behaviour in a robot soccer environment, PhD Thesis, RMIT University: Melbourne, Australia. (2005).
  17. Yanik, P., Ford, G. and McDaniel, W. 2010. An introduction and literature review of fuzzy logic applications for robot motion planning. In Proceedings of ASEE Southeast Section Conference. (2010).
Index Terms

Computer Science
Information Sciences

Keywords

Fuzzy Logic Mamdani Cricket Player Performance Evaluator Cricket Player Performance Evaluator