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Recursive time- and order-update algorithms for radial basis function networks.

机译：径向基函数网络的递归时间和顺序更新算法。

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摘要

Scope and method of study. This research develops online learning schemes that can construct small and parsimonious RBF networks. We have shown that this goal can be achieved by using the time- and order-update framework developed in this research. This framework is adopted by combining three schemes: time-update, order-update, and subset selection. Using this framework, two new algorithms, the Recursive Least Squares with Automatic Weight Selection (RLS-AWS) algorithm and the QR Recursive Least Squares with Automatic Weight Selection (QR-RLS-AWS), have been developed. Both algorithms are recursive in time and order. We first developed the subset selection mechanism of the algorithms based on the forward selection method. This technique allows useful RBF nodes to be added into the network sub-optimally and recursively. Later, we developed an improved subset selection mechanism based on the Efroymson method. This method has the capability of removing insignificant RBF nodes in addition to adding useful RBF nodes. Both recursive subset selection methods are new.; Findings and conclusions. The QR-RLS-AWS algorithm is numerical more accurate than the RLS-AWS algorithm. However, if numerical ill conditioning is not a problem, both algorithms yield the same solution. Both subset selection schemes, the recursive forward selection method and the recursive Efroymson method, have been adopted successfully. The results have shown that the recursive Efroymson method can produce smaller RBF network than the recursive forward selection method and the batch forward selection method. In conclusion, this research has successfully designed and implemented the recursive time- and order-update algorithms for online learning. Although the work described in this research has focused on small RBF networks, the algorithms can be applied to all linear models and all nonlinear models that have a linear-in-parameters structure, such as the fuzzy basis function network, functional-link network, polynomial network, and more.
机译：研究范围和方法。这项研究开发了可以构建小型且简约的RBF网络的在线学习方案。我们已经表明，可以使用本研究中开发的时间和订单更新框架来实现此目标。通过结合以下三种方案来采用此框架：时间更新，订单更新和子集选择。使用此框架，开发了两种新算法，即具有自动权重选择的递归最小二乘（RLS-AWS）算法和具有自动权重选择的QR递归最小二乘（QR-RLS-AWS）。两种算法在时间和顺序上都是递归的。我们首先开发了基于正向选择方法的算法子集选择机制。该技术允许将有用的RBF节点次优和递归地添加到网络中。后来，我们开发了一种基于Efroymson方法的改进子集选择机制。该方法除了添加有用的RBF节点外，还具有删除无关紧要的RBF节点的能力。两种递归子集选择方法都是新的。 发现和结论。 QR-RLS-AWS算法在数值上比RLS-AWS算法更精确。但是，如果数字病状问题不成问题，则两种算法都会得出相同的解。两种子集选择方案，递归正向选择方法和递归Efroymson方法，均已成功采用。结果表明，与递归正向选择方法和批量正向选择方法相比，递归Efroymson方法可以产生较小的RBF网络。总之，本研究成功设计并实现了在线学习的递归时间和顺序更新算法。尽管本研究中介绍的工作集中在小型RBF网络上，但是该算法可以应用于具有线性参数结构的所有线性模型和所有非线性模型，例如模糊基函数网络，功能链接网络，多项式网络等等。

著录项

  • 作者

    Fun, Meng Hock.;

  • 作者单位

    Oklahoma State University.;

  • 授予单位 Oklahoma State University.;
  • 学科 Engineering Electronics and Electrical.
  • 学位 Ph.D.
  • 年度 2001
  • 页码 233 p.
  • 总页数 233
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 无线电电子学、电信技术;
  • 关键词

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