Similar articles

Abstract: In this paper, a novel bayesian-Gaussian neural network (BGNN) is proposed and applied to on-line modeling of a hydraulic turbine system (HTS). The new BGNN takes account of the complex nonlinear characteristics of HTS. Two redefined training procedures of the BGNN include the off-line training of the threshold matrix parameters, optimized by swarm optimization algorithms, and the on-line BGNN predictive application driven by the sliding window data method. The characteristics models of an HTS are identified using the new BGNN method and simulation results are presented which show the effectiveness of the BGNN in addressing modeling problems of HTS.

[1] Caccavale, F., Pierri F., Villani, L., 2008. Adaptive observer for fault diagnosis in nonlinear discrete-time systems. J. Dynam. Syst. Meas. Control, 130(2):021005.

[2] Chang, J., Xiao, Z.H., Wang, S.Q., 2003. Neural Network Predict Control for the Hydroturbine Generator Set. Proc. 2nd Int. Conf. on Machine Learning and Cybernetics, p.540-543.

[3] Chen, T.P., Chen, H., 1993. Approximations of continuous functionals by neural networks with application to dynamic systems. IEEE Trans. Neur. Networks, 4(6):910-918.

[4] Chen, Y.H., Ye, L.Q., Cai, W.Y., 2003. Modeling of hydro-turbine hill chart by neural network. Chin. J. Huazhong Univ. Sci. Technol. (Nat. Sci. Ed.), 31(6):68-70 (in Chinese).

[5] Fang, Y.J., Liu, Y.J., 2008. Design of Automated Control System Based on Improved E. coli Foraging Optimization. Proc. IEEE Int. Conf. on Automation and Logistics, p.238-243.

[6] Huang, C.L., Wang, C.J., 2006. A GA-based feature selection and parameters optimization for support vector machines. Exp. Syst. Appl., 31(2):231-240.

[7] Ihme, M., Marsden, A.L., Pitsch, H., 2008. Generation of optimal artificial neural networks using a pattern search algorithm: application to approximation of chemical systems. Neur. Comput., 20(2):573-601.

[8] Jung, S.M., Ghaboussi, J., 2006. Neural network constitutive model for rate-dependent materials. Comput. & Struct., 84(15-16):955-963.

[9] Li, J.X., Sun, M.F., Yue, X.N., Zuo, G.T., 2009. Transient process research of hydraulic unit based on Matlab. GX Water Res. Hydr. Eng., 38(1):77-81 (in Chinese).

[10] Liu, B., Wang, L., Jin, Y.H., 2007. An effective PSO-based memetic algorithm for flow shop scheduling. IEEE Trans. Syst. Man Cybern., 31(1):18-27.

[11] Pei, J.S., Mai, E.C., 2008. Constructing multilayer feedforward neural networks to approximate nonlinear functions in engineering mechanics applications. J. Appl. Mech., 75(6):061002.

[12] Sarimveis, H., 2000. Training algorithms and learning abilities of three different types of artificial neural networks. Syst. Anal. Model. Simul., 38(3):555-581.

[13] Shen, Z.Y., 1996. Analysis of Hydraulic Turbine Governing System. Water Resources and Electric Power Press, Beijing, China (in Chinese).

[14] Shirvany, Y., Hayati, M., Moradian, R., 2008. Numerical solution of the nonlinear schrodinger equation by feedforward neural networks. Commun. Nonl. Sci. Numer. Simul., 13(10):2132-2145.

[15] Wang, S.Q., Liu, H., Zhang Z.P., Liu S.Y., 2008. Research on the Intelligent Control Strategy based on FNNC and GAs for Hydraulic Turbine Generating Units. Proc. 7th World Congress on Intelligent Control and Automation, p.5569-5573.

[16] Xiao, Z.H., Wang, S.Q., Zeng, H.T., Yuan, X.H., 2006. Identifying of Hydraulic Turbine Generating Unit Model Based on Neural Network. Proc. 6th Int. Conf. on Intelligent Systems Design and Applications, p.113-117.

[17] Ye, H.W., Nicolai, R., Reh, L., 1998. A Bayesian-Gaussian neural network and its application in process engineering. Chem. Eng. Process, 37(5):439-449.