A Fast Adaptive Tunable RBF Network For Nonstationary Systems
提出一种在线学习的径向基函数网络方法，通过多新息递推最小二乘调整权重，并在残差过大时替换贡献小的节点，优化新节点参数，显著提升非平稳系统的建模性能。
This paper describes a novel on-line learning approach for radial basis function (RBF) neural network. Based on an RBF network with individually tunable nodes and a fixed small model size, the weight vector is adjusted using the multi-innovation recursive least square algorithm on-line. When the residual error of the RBF network becomes large despite of the weight adaptation, an insignificant node with little contribution to the overall system is replaced by a new node. Structural parameters of the new node are optimized by proposed fast algorithms in order to significantly improve the modeling performance. The proposed scheme describes a novel, flexible, and fast way for on-line system identification problems. Simulation results show that the proposed approach can significantly outperform existing ones for nonstationary systems in particular.