Adaptive RBFN model for 2D spatial interpolation

As known, radial basis function (RBF) network is considered as an effective methodology to make prediction in spatial space, with spatial information fusion at different layers of RBF; the hidden layers fusion is able to give better result. The novelty of this paper is to propose an adaptive RBF network construction method, which combines the traditional incremental algorithm and real-time responsivity analysis. In the process of training, classification output error rate will be calculated to evaluate the responsivity. Experiments were carried out with practical weather sites data sets based on three other algorithms, i.e. Voronoi diagram, IDW (inverse distance weighted), topogrid algorithm. Compared results have shown that the proposed method has advantages in terms of both performance and precision. In addition, the adaptive attributes make it convenient to implement interpolation between variational source data sets.