Vladimir Ceperic

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—This paper presents a generic strategy for short-term load forecasting (STLF) based on the support vector regression machines (SVR). Two important improvements to the SVR based load forecasting method are introduced, i.e., procedure for generation of model inputs and subsequent model input selection using feature selection algorithms. One of the objectives(More)
In this paper the use of sparse linear regression algorithms in echo state networks (ESN) is presented for reducing the number of readouts and improving the robustness and generalization properties of ESNs. Three data sets with overall 80 tests are used to validate the use of sparse linear regression algorithms for echo state networks. It is shown that it(More)
This paper describes an implementation of a proportional-integral-derivative (PID) controller with an iterative error compensator for a DC brushless motor to improve the quality of a photocopier. The parameters of the iterative error compensator are determined by using particle swarm optimization algorithm. The proposed controller reduces the effects of(More)
In this paper the use of support vector regression (SVR) machines for modelling of nonlinear dynamic behaviour of an AC-DC rectifier is presented. The use of SVR machines yields a black-box model which significantly reduces the simulation time. The simulated AC-DC rectifier is commonly found in radio-frequency identification (RFID) circuits and it is an(More)