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The partitioning of a time-series into internally homogeneous segments is an important data mining problem. The changes of the variables of a multivariate time-series are usually vague and do not focus on any particular time point. Therefore it is not practical to define crisp bounds of the segments. Although fuzzy clustering algorithms are widely used to(More)
Selecting the order of an input-output model of a dynamical system is a key step toward the goal of system identification. The false nearest neighbors algorithm (FNN) is a useful tool for the estimation of the order of linear and nonlinear systems. While advanced FNN uses nonlinear input-output data based models for the model-based selection of the(More)
The segmentation of time-series is a constrained clustering problem: the data points should be grouped by their similarity, but with the constraint that all points in a cluster must come from successive time points. The changes of the variables of a time-series are usually vague and do not focused on any particular time point. Therefore it is not practical(More)
Segmentation is the most frequently used subroutine in clustering, indexing, sum-marization, anomaly detection, and classification of time series. Although in many real-life applications a lot of variables must be simultaneously monitored, most of the segmentation algorithms are used for the analysis of only one time-variant variable. Hence, this paper(More)
Selecting the order of an input-output model of a dynamical system is a key step toward the goal of system identification. By determining the smallest regression vector dimension that allows accurate prediction of the output, the false nearest neighbors algorithm (FNN) is a useful tool for linear and also for nonlinear systems. The one parameter that needs(More)
Nonlinear state estimation is a useful approach to the monitoring of industrial (polymerization) processes. This paper investigates how this approach can be followed to the development of a soft sensor of the product quality (melt index). The bottleneck of the successful application of advanced state estimation algorithms is the identification of models(More)
Time-series segmentation algorithms, such as methods based on Principal Component Analysis (PCA) and fuzzy clustering, are based on input-output process data. However, historical process data alone may not be sufficient for the monitoring of process transitions. Hence, the key idea of this paper is to incorporate the first-principle model based state(More)