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Application of Radial Basis Function Network for the Modeling and Simulation of Turbogenerator

Mohsen Hayati1, 2, 4, Abbas Rezaei3,4, and Leila Noori4
1. Electrical Engineering Department, Faculty of Engineering, Razi University, Kermanshah-67149, Iran
2. Computational Intelligence Research Centre, Faculty of Engineering, Razi University, Kermanshah-67149, Iran
3. Electrical Engineering Department, Kermanshah University of technology, Kermanshah, Iran
4. Department of biomedical Engineering, Faculty of medicine, Kermanshah University of medical sciences, Kermanshah, Iran
Abstract—In this paper, the applicability of Radial Basis Function (RBF) network for the modeling and simulation of turbogenerators is presented. It is expensive and timeconsuming to do experimental work to predict the behaviour of Turbogenerators with changing all variables. The RBF network is developed with speed and excitation current as inputs and voltage, active power and reactive power as desired outputs. The proposed RBF model is developed and trained with MATLAB 7.8 software. To obtain the optimal RBF model several structures have been constructed and tested. The comparison between experimental and predicted values using the proposed RBF model shows that there is a good agreement between them. Moreover, the RBF model is compared with another model named Multi Layer Perceptron (MLP), which is another important architectures of neural networks. The results obtained show that the proposed RBF model is more accurate and reliable than MLP model.

Index Terms— Radial basis function, Modelling, neural network, Turbogenerator

Cite: Mohsen Hayati, Abbas Rezaei, and Leila Noori, "Application of Radial Basis Function Network for the Modeling and Simulation of Turbogenerator," Journal of Advances in Information Technology, Vol. 4, No. 2, pp. 76-79, May, 2013.doi:10.4304/jait.4.2.76-79
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