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- János Abonyi, Balazs Feil, Sandor Z. Németh, Peter Arva
- Fuzzy Sets and Systems
- 2005

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)

- János Abonyi, Balazs Feil, Sandor Z. Németh, Peter Arva
- IDA
- 2003

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)

- János Abonyi, Robert Babugkao, Balazs Feil
- FUZZ-IEEE
- 2003

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)

- János Abonyi, Balazs Feil, Ajith Abraham
- Informatica
- 2005

This paper describes links between computational intelligence (CI), data mining and knowledge discovery. The generating elements of soft computing based data mining algorithms are defined where the extracted knowledge is represented by fuzzy rule-based expert systems. It is recognized that both model performance and interpretability are of major importance… (More)

- Balazs Feil, János Abonyi, Sandor Z. Németh, Peter Arva
- Computers & Chemical Engineering
- 2005

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)

- Balazs Feil, Balazs Balasko, János Abonyi
- Soft Comput.
- 2007

- Balazs Feil, János Abonyi, +4 authors Gabor Nagy
- ICAISC
- 2004

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)