Learning methods for radial basis function networks

Neruda R; Kudova P

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Learning methods for radial basis function networks

机译：径向基函数网络的学习方法

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摘要

RBF networks represent a vital alternative to the widely used multilayer perceptron neural networks. In this paper we present and examine several learning methods for RBF networks and their combinations. A gradient-based learning, the three-step algorithm with unsupervised part, and an evolutionary algorithms are introduced, and their performance compared on benchmark problems from the Proben1 database. The results show that the three-step learning is usually the fastest, while the gradient learning achieves better precision. The best results can be achieved by employing hybrid approaches that combine presented methods. (c) 2004 Elsevier B.V. All rights reserved.

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《Future generation computer systems》 |2005年第7期|p. 1131-1142|共12页
作者
Neruda R; Kudova P;
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作者单位

Acad Sci Czech Republ, Inst Comp Sci, Prague, Czech Republic;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种 eng
中图分类计算技术、计算机技术;
关键词
radial basis function networks; hybrid supervised learning; genetic algorithms; benchmarking;

机译：径向基函数网络混合监督学习遗传算法基准测试;

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2. LOGICAL RADIAL BASIS FUNCTION NETWORKS: A HYBRID MODEL FOR EFFICIENT IMPLEMENTATION OF CLASSICAL RADIAL BASIS FUNCTION NETWORKS [J] . H.A. Hefny, A.A. Bahnasawi, A.H. Abdel Wahab, Control and Intelligent Systems . 2002,第3期

机译：逻辑径向基函数网络：有效实现经典径向基函数网络的混合模型
3. RADIAL BASIS FUNCTION NEURAL NETWORKS FOR TRANSIENT STABILITY ASSESSMENT PART I: THEORY OF RADIAL BASIS FUNCTION NEURAL NETWORKS [J] . S. Muknahallipatna, B.H. Chowdhury International journal of power & energy systems . 2001,第2期

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4. Optimum design using radial basis function networks by adaptive range genetic algorithms (determination of radius in radial basis function networks) [C] . Masao Arakawa, Hirotaka Nakayama, Ye Boon Yun, Conference of the IEEE Industrial Electronics Society, Annual . 2000

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5. The scaled hyper-cube extended method for calculating the width of receptive field in Radial Basis Function Networks. [D] . Sran, Gagandeep Singh. 2015

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6. Novel Online Dimensionality Reduction Method with Improved Topology Representing and Radial Basis Function Networks [O] . Shengqiao Ni, Jiancheng Lv, Zhehao Cheng, -1

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7. Error-driven active learning in growing radial basis function networks for early robot learning, [O] . Meng Qinggang, Lee M. H. 2008

机译：在不断发展的径向基函数网络中由错误驱动的主动学习，用于早期机器人学习

Learning methods for radial basis function networks

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