论文信息 - Fuzzy association rule mining for model structure identification

Fuzzy association rule mining for model structure identification

Effective methods for model structure selection are very important for data-driven modelling, data mining, and system identification. A method for selecting regressors in nonlinear models with mixed discrete (categorical), fuzzy and continuous inputs and outputs is proposed based on fuzzy association rule mining. The selection of the important variables is based on the correlation measure of the fuzzy association rules.

János Abonyi | Attila Gyenesei | Ferenc Péter Pach | P. Arval

[1] L. A. Aguirre,et al. GLOBAL NONLINEAR POLYNOMIAL MODELS: STRUCTURE, TERM CLUSTERS AND FIXED POINTS , 1996 .

[2] Tzung-Pei Hong,et al. Mining association rules from quantitative data , 1999, Intell. Data Anal..

[3] Ramakrishnan Srikant,et al. Fast Algorithms for Mining Association Rules in Large Databases , 1994, VLDB.

[4] Donald Gustafson,et al. Fuzzy clustering with a fuzzy covariance matrix , 1978, 1978 IEEE Conference on Decision and Control including the 17th Symposium on Adaptive Processes.

[5] Man Hon Wong,et al. Mining fuzzy association rules in databases , 1998, SGMD.

[6] H. Akaike. A new look at the statistical model identification , 1974 .

[7] Ronald K. Pearson,et al. Nonlinear model-based control using second-order Volterra models , 1995, Autom..

[8] S. Billings,et al. Orthogonal parameter estimation algorithm for non-linear stochastic systems , 1988 .

[9] M. Morari,et al. Determining the model order of nonlinear input/output systems , 1998 .

[10] L. A. Aguirre,et al. Improved structure selection for nonlinear models based on term clustering , 1995 .

[11] Stephen A. Billings,et al. An alternative solution to the model structure selection problem , 2001, IEEE Trans. Syst. Man Cybern. Part A.

[12] D. Mitchell Wilkes,et al. ARMA model order estimation based on the eigenvalues of the covariance matrix , 1993, IEEE Trans. Signal Process..