A generalized growing and pruning RBF (GGAP-RBF) neural network for function approximation
A generalized growing and pruning RBF (GGAP-RBF) neural network for function approximation
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DOI：
10.1109/tnn.2004.836241
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发表时间：
2005-01-01
影响因子：
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通讯作者：
Sundararajan, N
中科院分区：
文献类型：
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作者：
Huang, GB;Saratchandran, P;Sundararajan, N
本文提出了一种用于径向基函数（RBF）网络的新的顺序学习算法，称为RBF的广义生长和修剪算法（GGAP - RBF）。本文首先介绍了隐藏神经元重要性的概念，然后在学习算法中使用它来实现简约网络。GGAP - RBF的生长和修剪策略是基于将所需的学习精度与最近的或有意添加的新神经元的重要性相关联。神经元的重要性是对该神经元平均信息含量的一种度量。GGAP - RBF算法可用于训练样本的任意采样密度，并且是从严格的统计学角度推导出来的。在函数逼近领域的基准问题的仿真结果表明，无论训练数据的采样密度函数如何，GGAP - RBF在学习速度、网络规模和泛化性能方面都优于其他几种顺序学习算法。
This paper presents a new sequential learning algorithm for radial basis function (RBF) networks referred. to as generalized growing and pruning algorithm for RBF (GGAP-RBF). The paper, first introduces the concept of significance for the hidden neurons and then uses it in the learning algorithm to realize parsimonious networks. The growing and pruning strategy of GGAP-RBF is based on linking the required learning accuracy with the significance of the nearest or intentionally added new neuron. Significance of a neuron is a measure of the average information content of that neuron. The GGAP-RBF algorithm can be used for any arbitrary sampling density for training samples and is derived from a rigorous statistical point of view. Simulation results for bench mark problems in the function approximation area show that the GGAP-RBF outperforms several other sequential learning algorithms in terms of learning speed, network size and generalization performance regardless of the sampling density function of the training data.