在许多工程实践问题中，区间回归分析是处理区间数据的一个重要手段。文献中用神经网络实现的区间回归分析都假定给定的训练数据是无噪的。
A method by means of neural networks for interval regression analysis hasbeen proposed on the assumption that the given training data are totally "good data".
结果表明，在无噪和有噪情况下，神经网络模型的辨识精度和泛化能力都要优于传统方法。
Compared with the classical method, the identification accuracy and the generalization capability of nn are testified to be superior in either the free - noise or noisy case.
介绍低相噪NPLL频率综合器的设计及实验结果。提出用无源环路滤波器比用有源环路滤波器更好，可获得低相噪设计。
This paper gives the design and experimental results of low noise NPLL frequency synthesizer and concludes that the passive loop filter is more suitable for low noise design than active loop filter.
高密度数据的无假频特征更加适合于常规二维信噪分离方法。
Seismic data of dense spatial sample is more suitable to conventional 2-D methods for separation of signal and noise.
高密度数据的无假频特征更加适合于常规二维信噪分离方法。
Seismic data of dense spatial sample is more suitable to conventional 2-D methods for separation of signal and noise.
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