Kalman filtering with real-time applications
Kalman filtering with real-time applications
Neurofuzzy adaptive modelling and control
Neurofuzzy adaptive modelling and control
Genetic programming for model selection of TSK-fuzzy systems
Information Sciences: an International Journal - Recent advances in genetic fuzzy systems
Fuzzy local linearization and local basis function expansion innonlinear system modeling
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Improving the interpretability of TSK fuzzy models by combining global learning and local learning
IEEE Transactions on Fuzzy Systems
On the interpretation and identification of dynamic Takagi-Sugeno fuzzy models
IEEE Transactions on Fuzzy Systems
A hybrid learning scheme combining EM and MASMOD algorithms for fuzzy local linearization modeling
IEEE Transactions on Neural Networks
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Cerebellar model articulation controller (CMAC) has been widely applied to modeling and control due to its attractive features such as fast training speed and parsimonious structure. A parametric CMAC is a CMAC model with its constant weights replaced by linear functional weights or linear local models, i.e., a type of Tagaki-Sugeno fuzzy model. This paper proposes a regularized parametric CMAC, and investigates how its linear local models are able to approximate the local linearity of the nonlinear system to be modeled by using regularization techniques and how the regularized parametric CMAC can be successfully applied in modeling a nonlinear process for state estimation of unknown nonlinear processes. Experimental results on the approximation ability and interpretability of the regularized parametric CMAC and its application to nonlinear state estimation have been presented to demonstrate the advantages of the regularized parametric CMAC.