| 引用本文: | 丁 勇, 刘守生, 胡寿松.一种广义小波神经网络的结构及其优化方法[J].控制理论与应用,2003,20(1):125~128.[点击复制] |
| DING Yong, LIU Shou-sheng, HU Shou-song.Extended wavelet neural network structure and its optimal method[J].Control Theory and Technology,2003,20(1):125~128.[点击复制] |
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| 一种广义小波神经网络的结构及其优化方法 |
| Extended wavelet neural network structure and its optimal method |
| 摘要点击 1025 全文点击 1762 投稿时间：2000-11-06 修订日期：2002-02-18 |
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| DOI编号 |
| 2003,20(1):125-128 |
| 中文关键词 小波框架 主成份分析 广义小波神经网络 |
| 英文关键词 wavelet frame PCA EWNN |
| 基金项目 国家自然科学重点基金(60234010); 高校博士点基金(2000028704); 南航青年科学基金(S9919305)资助项目 |
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| 中文摘要 |
| 从理论上分析了小波神经网络节点过多及鲁棒性差的原因,基于主成份分析 (PCA)的思想提出了一种规模小、抗干扰性强的广义小波神经网络(EWNN)及其优化方法.仿真结果表明,用该方法设计的广义小波神经网络,其非线性逼近能力及稳定性都明显优于普通小波神经网络. |
| 英文摘要 |
| The problems for wavelet neural network with large scale of nods and poor robustness are analyzed. An extended wavelet neural network (EWNN) and its optimal method, which has fewer nods and strong obstructive resistance, are designed on the basis of the principal component analysis (PCA). The simulation results show that EWNN is superior in nonlinear approximation and stability to the average wavelet network. |
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