This paper is about applying recurrent least squares support vector machines (LS-SVM) on three ES... more This paper is about applying recurrent least squares support vector machines (LS-SVM) on three ESTSP08 competition datasets. Least squares support vector machines are used as nonlinear models in order to avoid local minima problems. Then prediction task is re-formulated as function approximation task. Recurrent LS-SVM uses nonlinear autoregressive exogenous (NARX) model to build nonlinear regressor, by estimating in each iteration the next output value, given the past output and input measurements.
Preliminary notes This research paper presents the approach of automated computerized identificat... more Preliminary notes This research paper presents the approach of automated computerized identification of causal knowledge and causal graphs using monitoring of vibrations and temperatures of sliding bearings of high-power and high-speed process ventilators. Method of Granger causal connectivity analysis of vibration and temperature parameters is presented. This method improves diagnostics of process ventilators because of identification of causal relations and links of vibrations and temperatures in graph form. After computing and plotting causal graphs for vibrations and temperatures, causal density is computed as a measure of dynamical complexity of system. Numerical values of causal density are taken as indicators of systems "health" of process ventilators.
This paper is about applying recurrent least squares support vector machines (LS-SVM) on three ES... more This paper is about applying recurrent least squares support vector machines (LS-SVM) on three ESTSP08 competition datasets. Least squares support vector machines are used as nonlinear models in order to avoid local minima problems. Then prediction task is re-formulated as function approximation task. Recurrent LS-SVM uses nonlinear autoregressive exogenous (NARX) model to build nonlinear regressor, by estimating in each iteration the next output value, given the past output and input measurements.
Preliminary notes This research paper presents the approach of automated computerized identificat... more Preliminary notes This research paper presents the approach of automated computerized identification of causal knowledge and causal graphs using monitoring of vibrations and temperatures of sliding bearings of high-power and high-speed process ventilators. Method of Granger causal connectivity analysis of vibration and temperature parameters is presented. This method improves diagnostics of process ventilators because of identification of causal relations and links of vibrations and temperatures in graph form. After computing and plotting causal graphs for vibrations and temperatures, causal density is computed as a measure of dynamical complexity of system. Numerical values of causal density are taken as indicators of systems "health" of process ventilators.
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Papers by Indir Jaganjac