Two-stage RBF network construction based on particle swarm optimization
Two-stage RBF network construction based on particle swarm optimization
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DOI：
10.1177/0142331211403795
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发表时间：
2013-02
影响因子：
1.8
通讯作者：
Jing Deng;Kang Li;G. Irwin;M. Fei
中科院分区：
文献类型：
--
作者：
Jing Deng;Kang Li;G. Irwin;M. Fei
传统的径向基函数（RBF）网络优化方法，例如正交最小二乘法或两阶段选择，可以产生具有令人满意的泛化能力的稀疏网络。然而，RBF宽度作为网络中的非线性参数，并不容易确定。在上述方法中，宽度总是通过反复试验或随机生成来预先确定的。此外，所有隐藏节点共享相同的 RBF 宽度。这将不可避免地降低网络性能，并且可能需要更多的 RBF 中心来满足所需的建模规范。在本文中，我们研究了一种新的 RBF 网络两阶段构建算法。它利用粒子群优化方法来搜索最佳 RBF 中心及其相关宽度。尽管新方法比传统方法需要更多的计算量，但它可以大大减小模型大小并提高模型泛化性能。两个数值模拟例子证实了所提出技术的有效性。
The conventional radial basis function (RBF) network optimization methods, such as orthogonal least squares or the two-stage selection, can produce a sparse network with satisfactory generalization capability. However, the RBF width, as a nonlinear parameter in the network, is not easy to determine. In the aforementioned methods, the width is always pre-determined, either by trial-and-error, or generated randomly. Furthermore, all hidden nodes share the same RBF width. This will inevitably reduce the network performance, and more RBF centres may then be needed to meet a desired modelling specification. In this paper we investigate a new two-stage construction algorithm for RBF networks. It utilizes the particle swarm optimization method to search for the optimal RBF centres and their associated widths. Although the new method needs more computation than conventional approaches, it can greatly reduce the model size and improve model generalization performance. The effectiveness of the proposed technique is confirmed by two numerical simulation examples.