College of Electronic and Control Engineering, Beijing University of Technology, Beijing, 100124, China.
Int J Neural Syst. 2010 Feb;20(1):63-74. doi: 10.1142/S0129065710002243.
This paper presents a repair algorithm for the design of a Radial Basis Function (RBF) neural network. The proposed repair RBF (RRBF) algorithm starts from a single prototype randomly initialized in the feature space. The algorithm has two main phases: an architecture learning phase and a parameter adjustment phase. The architecture learning phase uses a repair strategy based on a sensitivity analysis (SA) of the network's output to judge when and where hidden nodes should be added to the network. New nodes are added to repair the architecture when the prototype does not meet the requirements. The parameter adjustment phase uses an adjustment strategy where the capabilities of the network are improved by modifying all the weights. The algorithm is applied to two application areas: approximating a non-linear function, and modeling the key parameter, chemical oxygen demand (COD) used in the waste water treatment process. The results of simulation show that the algorithm provides an efficient solution to both problems.
本文提出了一种用于径向基函数（RBF）神经网络设计的修复算法。所提出的修复 RBF（RRBF）算法从特征空间中随机初始化的单个原型开始。该算法有两个主要阶段：体系结构学习阶段和参数调整阶段。体系结构学习阶段使用基于网络输出的灵敏度分析（SA）的修复策略来判断何时何地应向网络添加隐藏节点。当原型不符合要求时，将添加新节点以修复体系结构。参数调整阶段使用调整策略，通过修改所有权重来提高网络的能力。该算法应用于两个应用领域：逼近非线性函数和模拟废水处理过程中使用的关键参数化学需氧量（COD）。仿真结果表明，该算法为这两个问题提供了有效的解决方案。
