| 小波网络模型在渗流量预测中的应用 |
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| 引用本文： | 郭张军,宋汉周,彭鹏.小波网络模型在渗流量预测中的应用[J].勘察科学技术,2008(1):40-43. |
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| 作者姓名： | 郭张军 宋汉周 彭鹏 |
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| 作者单位： | 1. 陕西电力科学研究院水电技术研究所,西安市,710054
2. 河海大学地质及岩土工程系,南京市,210098 |
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| 基金项目： | 华东电网有限公司科技项目No.T0501 |
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| 摘 要： | 将小波理论与神经网络理论相结合,建立了小波网络模型,并应用于工程实例。它避免了BP神经网络结构设计的盲目性和局部最优等非线性优化问题,大大简化了训练,具有较强的函数学习能力和推广能力。实例计算结果表明,小波网络模型具有比BP神经网络收敛速度快、预测精度高等特点,因而具有广阔的应用前景。
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| 关 键 词： | 小波网络 渗流 监控模型 |
| 修稿时间： | 2007年5月25日 |
Application of Wavelet Network Model in Seepage Discharge Prediction |
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| Guo Zhangjun,Song Hanzhou,Peng Peng.Application of Wavelet Network Model in Seepage Discharge Prediction[J].Site Investigation Science and Technology,2008(1):40-43. |
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| Authors: | Guo Zhangjun Song Hanzhou Peng Peng |
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| Affiliation: | 1.Hydropower Technology Institute of Shanxi Electricial Power Science Research Institute 2.Geotechnical Department of Hohai University |
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| Abstract: | Wavelet neural network is a kind of neural network,which combines wavelet theory closely with neural network theory,and avoids both the blindness of framework designs for BP neural networks and the problem of nonlinear optimizations,such as local optimization.So it can greatly simplify the training of neural networks.It has better abilities in function learning and generalization.The results show that the wavelet neural network established is faster in convergence speed and more accurate in prediction than BP neural network.Therefore it has wide application prospect. |
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| Keywords: | wavelet network seepage monitoring model |
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