Nonlinear black-box modeling in system identification: a unified overview
Automatica (Journal of IFAC) - Special issue on trends in system identification
Fuzzy Modeling for Control
Machine Learning
Fuzzy Model Identification for Control
Fuzzy Model Identification for Control
Cluster Analysis for Data Mining and System Identification
Cluster Analysis for Data Mining and System Identification
Brief paper: Identification of dynamic systems using Piecewise-Affine basis function models
Automatica (Journal of IFAC)
Perspectives of fuzzy systems and control
Fuzzy Sets and Systems
Survey paper: A survey on industrial applications of fuzzy control
Computers in Industry
A Survey on Analysis and Design of Model-Based Fuzzy Control Systems
IEEE Transactions on Fuzzy Systems
Switching regression models and fuzzy clustering
IEEE Transactions on Fuzzy Systems
Identification of piecewise affine systems via mixed-integer programming
Automatica (Journal of IFAC)
On the hinge-finding algorithm for hingeing hyperplanes
IEEE Transactions on Information Theory
Hinging hyperplanes for regression, classification, and function approximation
IEEE Transactions on Information Theory
Decision trees can initialize radial-basis function networks
IEEE Transactions on Neural Networks
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Hierarchical fuzzy modeling techniques have great advantage since model accuracy and complexity can be easily controlled thanks to the transparent model structures. A novel tool for regression tree identification is proposed based on the synergistic combination of fuzzy c-regression clustering and the concept of hierarchical modeling. In a special case (c=2), fuzzy c-regression clustering can be used for identification of hinging hyperplane models. The proposed method recursively identifies a hinging hyperplane model that contains two linear submodels by partitioning operating region of one local linear model resulting a binary regression tree. Novel measures of model performance and complexity are developed to support the analysis and building of the proposed special model structure. Effectiveness of proposed model is demonstrated by benchmark regression datasets. Examples also demonstrate that the proposed model can effectively represent nonlinear dynamical systems. Thanks to the piecewise linear model structure the resulted regression tree can be easily utilized in model predictive control. A detailed application example related to the model predictive control of a water heater demonstrate that the proposed framework can be effectively used in modeling and control of dynamical systems.