| 偏最小二乘回归在微波效应预测中的应用 |
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| 引用本文： | 钟龙权, 马弘舸. 偏最小二乘回归在微波效应预测中的应用[J]. 强激光与粒子束, 2011, 23(05). |
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| 作者姓名： | 钟龙权 马弘舸 |
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| 作者单位： | 1.中国工程物理研究院 应用电子学研究所, 四川 绵阳 621 900;;;2.中国工程物理研究院 研究生部, 北京 1 00088 |
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| 摘 要： | 引入偏最小二乘回归(PLSR)原理和方法应用于微波效应实验数据的预测，得到的预测精度与自适应神经模糊推理网络(ANFIS)结果基本一致，平均相对误差小于3%。实例分析了PLSR方法与ANFIS方法对建模数据样本量的需求，在建模样本数较少条件下，PLSR所建模型的预测精度均高于ANFIS模型。因此PLSR方法更适用于微波效应小样本数据的预测，更有利于实际应用。
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| 关 键 词： | 小样本 偏最小二乘回归 微波效应 自适应神经模糊推理网络 |
Application of partial least-square regression to prediction of microwave effects |
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| zhong longquan, ma hongge. Application of partial least-square regression to prediction of microwave effects[J]. High Power Laser and Particle Beams, 2011, 23. |
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| Authors: | zhong longquan ma hongge |
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| Affiliation: | 1. Institute of Applied Electronics,CAEP,P.O.Box 919-1017,Mianyang 621900,China;;;2. Graduate School of China Academy of Engineering Physics,Beijing 100088,China |
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| Abstract: | The partial least-square regression (PLSR) method was introduced and implemented in the prediction of microwave effects. The results show that the PLSR method has an accuracy almost consistent with the adaptive neuro-fuzzy inference system (ANFIS) model’s, and their average relative error is less than 3%. The requirement for sample size of these two methods was analyzed. On the condition of small sample size, the PLSR model is more precise than the ANFIS model. Thus the PLSR method is more effective for data processing and prediction with small sample size of microwave effects. |
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| Keywords: | small sample size partial least-square regression microwave effects adaptive neuro-fuzzy inference system |
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