方向依赖动态过程的建模:维纳模型和神经网络的比较

IMTC/2002. Proceedings of the 19th IEEE Instrumentation and Measurement Technology Conference (IEEE Cat. No.00CH37276) Pub Date : 2002-08-07 DOI:10.1109/IMTC.2002.1006842

A. H. Tan, K. Godfrey

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引用次数: 4

摘要

使用维纳和神经网络模型的方向依赖过程的建模比较了几种不同的过程和三种不同类型的输入信号:伪随机二进制信号(prbs)，反向重复伪随机二进制信号(irprbs)和多重正弦(谐波和)信号。在电子鼻上的实验结果说明了所讨论的技术的适用性。

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Modelling of direction-dependent dynamic processes: a comparison of Wiener models and neural networks

The modelling of direction-dependent processes using Wiener and neural network models is compared for several different processes and for three different types of input signal: a pseudorandom binary signal (prbs), an inverse-repeat pseudo-random binary signal (irprbs) and a multisine (sum of harmonics) signal. Experimental results on an electronic nose are presented to illustrate the applicability of the techniques discussed.

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IMTC/2002. Proceedings of the 19th IEEE Instrumentation and Measurement Technology Conference (IEEE Cat. No.00CH37276)

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