Nonlinear model-based control using second-order Volterra models
Automatica (Journal of IFAC)
Mining fuzzy association rules in databases
ACM SIGMOD Record
Database Mining: A Performance Perspective
IEEE Transactions on Knowledge and Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
An alternative solution to the model structure selection problem
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Preknowledge-based generalized association rules mining
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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Effective methods for feature and model structure selection are very important for data-driven modeling, data mining, and system identification tasks. This paper presents a new method for selecting important variables (regressors) in nonlinear (dynamic) models with mixed discrete (categorical, fuzzy) and continuous inputs and outputs. The proposed method applies fuzzy association rule mining. The selection process of the important variables is based on two interesting measures of the mined association rules.