| 紧支撑小波神经网络用于钢的冷弯性能判别 |
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| 引用本文： | 印春生,任琴,张蕴慧,司圣柱,潘忠孝,张懋森.紧支撑小波神经网络用于钢的冷弯性能判别[J].计算机与应用化学,1999(6). |
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| 作者姓名： | 印春生 任琴 张蕴慧 司圣柱 潘忠孝 张懋森 |
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| 作者单位： | 中国科学技术大学应用化学系!合肥230026 |
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| 基金项目： | 国家自然科学基金!29775001 |
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| 摘 要： | 介绍了紧支撑小波神经网络（ＣＳＷＮＮ） 的理论和算法， 将其运用于钢的冷弯性能判别， 判别准确率达到１００ ％ 。与ＢＰ算法的计算值和文献中所采用的主成分分析（ＰＣＡ） 比较， ＣＳＷＮＮ 的判别能力优于ＢＰ及文献中采用的主成分分析。
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| 关 键 词： | 紧支撑小波神经网络 小波分析 钢的冷弯性能判别 |
DISCRIMINATION OF COOL BENDING NATURE OF STEEL BY USING COMPACTLY SUPPORTED WAVELET NEURAL NETWORK |
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| YIN Chun\|Sheng\ REN Qin\ ZHANG Yun\|Hui\ SI Sheng\|Zhu,PAN Zhong\|Xiao\ ZHANG,Mao\|Sen.DISCRIMINATION OF COOL BENDING NATURE OF STEEL BY USING COMPACTLY SUPPORTED WAVELET NEURAL NETWORK[J].Computers and Applied Chemistry,1999(6). |
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| Authors: | YIN Chun\|Sheng\ REN Qin\ ZHANG Yun\|Hui\ SI Sheng\|Zhu PAN Zhong\|Xiao\ ZHANG Mao\|Sen |
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| Abstract: | The theory and algorithm of compactly supported wavelet neural network (CSWNN) were introduced and applied to the discrimination of cool bending natures of steels. The discrimination accuracy is 100% with CSWNN. Predicted results indicated that CSWNN was better than the back\|propagation (BP) algorithm and the principal comporent analysis (PCA). |
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| Keywords: | Compactly supported wavelet neural network Wavelet analysis Discrimination Cool bending nature of steel |
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