Classification of stellar spectral data based on Kalman filter and RBF neural networks

In this paper, a novel stellar spectral classification technique is proposed. Which is composed of the following two steps: In the first step, Kalman filter is adopted to conduct de-noising process. At the same time, Kalman filter is also used for optimal feature extraction. The second step, radial basis function neural network is employed for the final classification. The proposed technique can be considered as a composite classifier which combines Kalman filter and radial basis function networks. The experiments show that our new technique is both robust and efficient, the obtained correct classification rate is much improved by the composite classifier, and these results are much better than the best results obtained from regularized discriminant analysis with principle component analysis data dimension reduction technique.