Basis Function Neural Network and Its Fast Optimal-structure Determination

XIAO Xiu-chun; JIANG Xiao-hua; ZHANG Yu-nong

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Basis Function Neural Network and Its Fast Optimal-structure Determination

更新时间：2026-09-04

- Basis Function Neural Network and Its Fast Optimal-structure Determination
- Vol. 27, Issue 1, Pages: 57-60(2010)
- 作者机构：
  
  1. 广东海洋大学机构信息学院,广东,湛江,524025
  2. 中山大学信息科学与技术学院,广东,广州,510275
  3. 浙江大学CAD/CG国家重点实验室,浙江,杭州,310058
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2010
- 稿件说明：
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肖秀春, 姜孝华, 张雨浓. 一种基函数神经网络最优隐神经元数目快速确定算法[J]. 微电子学与计算机, 2010, 27(1): 57-60.

XIAO Xiu-chun, JIANG Xiao-hua, ZHANG Yu-nong. Basis Function Neural Network and Its Fast Optimal-structure Determination[J]. 2010, 27(1): 57-60.
肖秀春, 姜孝华, 张雨浓. 一种基函数神经网络最优隐神经元数目快速确定算法[J]. 微电子学与计算机, 2010, 27(1): 57-60. DOI：

XIAO Xiu-chun, JIANG Xiao-hua, ZHANG Yu-nong. Basis Function Neural Network and Its Fast Optimal-structure Determination[J]. 2010, 27(1): 57-60. DOI：

摘要

以线性无关的基函数作为隐层神经元的激励函数

构建了一类基函数神经网络

且推导出该类神经网络的学习算法;在此基础上

设计了一种基于指数增长和折半删减的快速最小隐神经元数目确定算法.仿真实验表明

此算法能自适应地、快速有效地确定网络最小隐层神经元数目.

Abstract

In this paper

a basis function neural network is constructed

of which the hidden-layor neurons are activated with linear independence basis function.Accordingly

the learning algorithm for the constructed neural network is derived and a fast algorithm based on exponential-growth and binary-delete search strategy is proposed to determinate the optimal number of hidden-layor neurons.The simulation results substantiate that our algorithm can adaptively

quickly and efficiently determine number of hidden neurons in the neural network.

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