School of Systems Engineering, University of Reading, Reading, West Berkshire RG6 6UR, UK.
IEEE Trans Cybern. 2013 Jun;43(3):935-47. doi: 10.1109/TSMCB.2012.2218804. Epub 2012 Oct 18.
In this paper, we propose a novel online modeling algorithm for nonlinear and nonstationary systems using a radial basis function (RBF) neural network with a fixed number of hidden nodes. Each of the RBF basis functions has a tunable center vector and an adjustable diagonal covariance matrix. A multi-innovation recursive least square (MRLS) algorithm is applied to update the weights of RBF online, while the modeling performance is monitored. When the modeling residual of the RBF network becomes large in spite of the weight adaptation, a node identified as insignificant is replaced with a new node, for which the tunable center vector and diagonal covariance matrix are optimized using the quantum particle swarm optimization (QPSO) algorithm. The major contribution is to combine the MRLS weight adaptation and QPSO node structure optimization in an innovative way so that it can track well the local characteristic in the nonstationary system with a very sparse model. Simulation results show that the proposed algorithm has significantly better performance than existing approaches.
在本文中，我们提出了一种新的在线建模算法，用于使用具有固定数量隐藏节点的径向基函数（RBF）神经网络对非线性和非平稳系统进行建模。每个 RBF 基函数都有一个可调的中心向量和一个可调整的对角协方差矩阵。应用多创新递归最小二乘（MRLS）算法在线更新 RBF 的权重，同时监测建模性能。当 RBF 网络的建模残差尽管进行了权重自适应调整，但仍然很大时，将一个被识别为不重要的节点用一个新节点替换，该新节点的可调中心向量和对角协方差矩阵使用量子粒子群优化（QPSO）算法进行优化。主要贡献是以创新的方式将 MRLS 权重自适应和 QPSO 节点结构优化结合起来，以便用非常稀疏的模型很好地跟踪非平稳系统中的局部特征。仿真结果表明，所提出的算法性能明显优于现有方法。
