System identification: theory for the user
System identification: theory for the user
Decision estimation and classification: an introduction to pattern recognition and related topics
Decision estimation and classification: an introduction to pattern recognition and related topics
General hybrid dynamical systems: modeling, analysis, and control
Proceedings of the DIMACS/SYCON workshop on Hybrid systems III : verification and control: verification and control
A robust algorithm for automatic extraction of an unknown number of clusters from noisy data
Pattern Recognition Letters
Incremental learning in fuzzy pattern matching
Fuzzy Sets and Systems - Possibility theory and fuzzy logic
Clustering by competitive agglomeration
Pattern Recognition
Brief Equivalence of hybrid dynamical models
Automatica (Journal of IFAC)
A clustering technique for the identification of piecewise affine systems
Automatica (Journal of IFAC)
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The behaviours of hybrid dynamic systems (HDS) are determined by combining continuous variables with discrete switching logic. The identification of a HDS aims to find an accurate model of the system's dynamics based on its past inputs and outputs. In pattern recognition (PR) methods, each mode is represented by a set of similar patterns that form restricted regions in the feature space. These sets of patterns are called classes. A pattern is a vector built from past inputs and outputs. HDS identification is a challenging problem since it involves the estimation of different sets of parameters without knowing in advance which sections of the measured data correspond to the different modes of the system. Therefore, HDS identification can be achieved by combining two steps: clustering and parameter estimation. In the clustering step, the number of discrete modes (i.e., the classes that input-output data points belong) is estimated. The parameter estimation step finds the parameters of the models that govern the continuous dynamics in each mode. In this paper, an unsupervised PR method is proposed to achieve the clustering step of the identification of temporally switched linear HDS. The determination of the number of modes does not require prior information about the modes or their number.