Adaptive RBF-AR Models Based on Multi-Innovation Least Squares Method
Min Gan, Xiaoxian Chen, Feng Ding, Guangyong Chen, C. L. Philip Chen · IEEE Signal Processing Letters · 2019
In the previous work, the parameters of radial basis function network based autoregressive (RBF-AR) models are estimated offline and no longer updated afterward. In this letter, an adaptive learning algorithm is proposed for the RBF-AR models. The proposed strategy is that the nonlinear parameters are previously determined by an off-line variable projection method; and once new samples are available, the linear parameters are updated. The linear adaptive algorithm adopted in this letter is the multi-innovation least squares method, due to its high performance. The simulation results show that with the adaption of the linear parameters, the prediction performance of the RBF-AR models may be significantly improved, which demonstrates the effectiveness of the proposed algorithm.