Identification of non-linear system structure and parameters using regime decomposition
Automatica (Journal of IFAC)
The nature of statistical learning theory
The nature of statistical learning theory
Interconnected automata and linear systems: a theoretical framework in discrete-time
Proceedings of the DIMACS/SYCON workshop on Hybrid systems III : verification and control: verification and control
System identification (2nd ed.): theory for the user
System identification (2nd ed.): theory for the user
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Verification of Hybrid Systems via Mathematical Programming
HSCC '99 Proceedings of the Second International Workshop on Hybrid Systems: Computation and Control
Piecewise Linear Homeomorphisms: The Scalar Case
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 3 - Volume 3
A Structure Trainable Neural Network with Embedded Gating Units and Its Learning Algorithm
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 3 - Volume 3
Control of systems integrating logic, dynamics, and constraints
Automatica (Journal of IFAC)
A New Learning Method for Piecewise Linear Regression
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
A clustering technique for the identification of piecewise affine systems
Automatica (Journal of IFAC)
Set Membership identification of nonlinear systems
Automatica (Journal of IFAC)
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We propose a new technique for the identification of discrete-time hybrid systems in the Piece-Wise Affine (PWA) form. The identification algorithm proposed in [10] is first considered and then improved under various aspects. Measures of confidence on the samples are introduced and exploited in order to improve the performance of both the clustering algorithm used for classifying the data and the final linear regression procedure. Moreover, clustering is performed in a suitably defined space that allows also to reconstruct different submodels that share the same coefficients but are defined on different regions.