Title

Optimisation of neural state variables estimators of two-mass drive system using the Bayesian regularization method

Journal title

Bulletin of the Polish Academy of Sciences Technical Sciences

Yearbook

2011

Volume

59

Issue

No 1

Authors

Kamiński, M. ; Orlowska-Kowalska, T.

Divisions of PAS

Nauki Techniczne

Coverage

33-38

Date

2011

Identifier

DOI: 10.2478/v10175-011-0006-1 ; ISSN 2300-1917

Source

Bulletin of the Polish Academy of Sciences: Technical Sciences; 2011; 59; No 1; 33-38

References

Cristea M. (2001), A new neural networks approach to induction motor speed control, IEEE Power Electronics Specialists Conf, 2, 784. ; D'Angel M. (2000), State estimation for induction machines using a neural network back-propagation technique, IEEE Int. Conf. on Systems, Man, and Cybernetics, 4, 2613. ; Grzesiak L. (2006), Energy flow control system based on neural compensator in the feedback path for autonomous energy source, Bull. Pol. Ac.: Tech, 54, 3, 335. ; Korbicz J. (2006), Robust fault detection using analytical and soft computing methods, Bull. Pol. Ac.: Tech, 54, 1, 75. ; Zhang G. (2000), Speed control of two-inertia system by PI/PID control, IEEE Trans. on Industrial Electronic, 47, 3, 603, doi.org/10.1109/41.847901 ; Orlowska-Kowalska T. (2007), Vibration suppression in two-mass drive system using PI speed controller and additional feedbacks - comparative study, IEEE Trans. Ind. Electronics, 54, 2, 1193, doi.org/10.1109/TIE.2007.892608 ; Szabat K. (2006), Extended Kalman filters in the control structure of two-mass drive system, Bull. Pol. Ac.: Tech, 54, 3, 315. ; Hush D. (1993), Progress in supervised neural networks, IEEE Signal Processing Magazine, 10, 1, 8, doi.org/10.1109/79.180705 ; MacKay D. (1992), Bayesian Interpolation, Neural Computation, 4, 3, 415, doi.org/10.1162/neco.1992.4.3.415 ; MacKay D. (1992), A practical Bayesian framework for back-propagation networks, Neural Computation, 4, 3, 448, doi.org/10.1162/neco.1992.4.3.448 ; F. Dan Foresee (1993), Gauss-Newton approximation to Bayesian learning, IEEE Int. Conf. on Neural Networks, 3, 1930. ; Mirikitani D. (2007), Recursive Bayesian Levenberg-Marquardt training of recurrent neural networks, Int. Joint Conf. on Neural Networks, 1, 282, doi.org/10.1109/IJCNN.2007.4370969 ; Gencay R. (2001), Pricing and hedging derivative securities with neural networks: Bayesian regularization, early stopping, and bagging, IEEE Trans. Ind. Electronics, 12, 4, 726. ; Aggarwal K. (2005), Bayesian regularization in a neural network model to estimate lines of code using function points, J. Computer Sciences, 1, 4, 505, doi.org/10.3844/jcssp.2005.505.509 ; Orlowska-Kowalska T. (2008), Mechanical state variable estimation of the drive system with elastic coupling using optimised MLP neural networks, Bull. Pol. Ac.: Tech, 56, 3, 239. ; Bishop C. (1996), Neural Networks for Pattern Recognition. ; Girosi F. (1995), Regularization theory and neural networks architectures, Neural Computation, 7, 2, 219, doi.org/10.1162/neco.1995.7.2.219