Gomm J B, Yu D L
Control Systems Research Group, School of Engineering, Liverpool John Moores University, Liverpool L3 3AF, UK.
IEEE Trans Neural Netw. 2000;11(2):306-14. doi: 10.1109/72.839002.
Recursive orthogonal least squares (ROLS) is a numerically robust method for solving for the output layer weights of a radial basis function (RBF) network, and requires less computer memory than the batch alternative. In this paper, the use of ROLS is extended to selecting the centers of an RBF network. It is shown that the information available in an ROLS algorithm after network training can be used to sequentially select centers to minimize the network output error. This provides efficient methods for network reduction to achieve smaller architectures with acceptable accuracy and without retraining. Two selection methods are developed, forward and backward. The methods are illustrated in applications of RBF networks to modeling a nonlinear time series and a real multiinput-multioutput chemical process. The final network models obtained achieve acceptable accuracy with significant reductions in the number of required centers.
递归正交最小二乘法（ROLS）是一种用于求解径向基函数（RBF）网络输出层权重的数值稳健方法，并且比批处理方法所需的计算机内存更少。在本文中，ROLS的应用扩展到了RBF网络中心的选择。结果表明，网络训练后ROLS算法中可用的信息可用于顺序选择中心，以最小化网络输出误差。这为网络简化提供了有效的方法，能够在不重新训练的情况下实现具有可接受精度的更小架构。开发了两种选择方法，即前向法和后向法。这些方法在RBF网络用于非线性时间序列建模和实际多输入多输出化学过程建模的应用中得到了说明。最终获得的网络模型在所需中心数量显著减少的情况下达到了可接受的精度。
