TY - GEN
T1 - Parametric tuning of rule-based systems by maximum fuzzy entropy
AU - DONG, Chun-Ru
AU - WANG, Ran
AU - WANG, Xi-Zhao
N1 - This work is financially supported by the natural science foundation of Hebei Province (F2008000635), by the plan of first 100 excellent scientists of Hebei Province and by the Hitech project of educational committee of Hebei Province (04213533).
PY - 2008
Y1 - 2008
N2 - Fuzzy Production Rules (FPRs) are widely used in expert systems to represent uncertainty concepts. In order to enhance the representation capability and to improve the reasoning-accuracy of FPRs, some useful knowledge representation parameters such as certainty factor, local weight and global weight have been included in FPRs. However, the acquisition of the values of these parameters is difficult and time-consuming. Usually the principle to determine these parameters is to further reduce the training error. This paper proposes a new principle, i.e., the maximum entropy principle, for solving these parameters. Firstly we present a parametric tuning method based on the maximization of fuzzy entropy on the training set, then a genetic algorithm-based optimization technique is applied to determine the values of the weights in FPRs. Experimental results demonstrate a number of advantages of our method such as automatic acquisition of the weights, avoiding the over-fitting to a great extent and non-changing the number of the initial FPRs.
AB - Fuzzy Production Rules (FPRs) are widely used in expert systems to represent uncertainty concepts. In order to enhance the representation capability and to improve the reasoning-accuracy of FPRs, some useful knowledge representation parameters such as certainty factor, local weight and global weight have been included in FPRs. However, the acquisition of the values of these parameters is difficult and time-consuming. Usually the principle to determine these parameters is to further reduce the training error. This paper proposes a new principle, i.e., the maximum entropy principle, for solving these parameters. Firstly we present a parametric tuning method based on the maximization of fuzzy entropy on the training set, then a genetic algorithm-based optimization technique is applied to determine the values of the weights in FPRs. Experimental results demonstrate a number of advantages of our method such as automatic acquisition of the weights, avoiding the over-fitting to a great extent and non-changing the number of the initial FPRs.
KW - Fuzzy production rules
KW - Maximum fuzzy entropy
KW - Overfitting
KW - Parameters refinement
KW - Weight
UR - http://www.scopus.com/inward/record.url?scp=69949135370&partnerID=8YFLogxK
U2 - 10.1109/ICSMC.2008.4811314
DO - 10.1109/ICSMC.2008.4811314
M3 - Conference paper (refereed)
AN - SCOPUS:69949135370
SN - 9781424423835
T3 - IEEE International Conference on Systems, Man and Cybernetics
SP - 433
EP - 438
BT - Proceedings : 2008 IEEE International Conference on Systems, Man and Cybernetics, SMC 2008
PB - IEEE
T2 - 2008 IEEE International Conference on Systems, Man and Cybernetics, SMC 2008
Y2 - 12 October 2008 through 15 October 2008
ER -