神经网络空间映射结构的研究与改进

计算机应用 ›› 2014, Vol. 34 ›› Issue (12): 3621-3623.

神经网络空间映射结构的研究与改进

闫淑霞,张齐军

天津大学电子信息工程学院,天津 300072

收稿日期:2014-06-19 修回日期:2014-08-12 出版日期:2014-12-01 发布日期:2014-12-31
通讯作者: 闫淑霞
作者简介:闫淑霞(1987-）,女,山东德州人,博士研究生,主要研究方向:基于神经网络技术的器件建模；张齐军(1959-）,男,山西长治人,教授,博士,主要研究方向:射频微波领域电路设计及建模
基金资助:
国家自然科学基金资助项目

Research and improvement of neuro-space mapping structure

YAN Shuxia,ZHANG Qijun

School of Electronic Information Engineering, Tianjin University, Tianjin 300072,China

Received:2014-06-19 Revised:2014-08-12 Online:2014-12-01 Published:2014-12-31
Contact: YAN Shuxia

摘要/Abstract

摘要：

针对粗模型与器件的直流特性差异较大而交流特性相似时建模过程复杂的问题,对已有神经网络空间映射(Neuro-SM)结构进行了改进。改进的模型在Neuro-SM结构基础上,增加电容和电感,使映射网络仅调整输入信号中的直流分量,不影响交流分量。在不改变粗模型交流特性的情况下改进直流特性,用少量的优化变量和简单的映射关系即可达到模型匹配的效果。通过仿真实验表明,改进后的Neuro-SM模型充分利用粗模型与器件非线性响应相似的特点,既保持了模型的精度又简化了建模过程。

Abstract:

In some cases, the difference of DC responses between the coarse model and devices is large, however the nonlinear responses are similar. Concerning the complex modeling process, an improved Neuro-Space Mapping (Neuro-SM) structure was proposed. The capacitors and inductors were added on the traditional Neuro-SM model to constitute a new Neuro-SM model. The DC component of the input signal was adjusted by the mapping network,but the AC component is independent on the mapping network. The new model can improve the DC feature without changing AC characteristic and match the device with a few optimization variables and simple mapping relationship. The simulation experimental results demonstrate that the enhanced Neuro-SM model can make full use of the similar nonlinear responses between the coarse model and devices, maintaining the accuracy of the model as well as simplifying the modeling process.

中图分类号:

TP183

闫淑霞张齐军. 神经网络空间映射结构的研究与改进[J]. 计算机应用, 2014, 34(12): 3621-3623.

YAN Shuxia ZHANG Qijun. Research and improvement of neuro-space mapping structure[J]. Journal of Computer Applications, 2014, 34(12): 3621-3623.

参考文献

［1］ZHANG Q J, GUPTA K C. Neural networks for RF and microwave design ［M］. Boston: Artech House, 2000:3-4.
［2］KABIR H, ZHANG L, YU M, et al. Smart modeling of microwave devices ［J］. IEEE Microwave Magazine, 2010, 11(3): 105-118.
［3］TIAN Y, ZHANG Q. Knowledge-based neural networks for modeling of radio-frequency/microwave components ［J］. Journal of University of Electronic Science and Technology of China, 2011, 40(6): 815-824. (田毅贞,张齐军. 知识型神经网络的射频/微波器件建模方法［J］. 电子科技大学学报,2011, 40(6): 815-824.)
［4］KABIR H, WANG Y, YU M, et al. Neural network inverse modeling and applications to microwave filter design ［J］. IEEE Transactions on Microwave Theory and Techniques, 2008, 56(4): 867-879.
［5］GARCIA J P, PEREIRA F Q, REBENAQUE D C, et al. A neural-network method for the analysis of multilayered shielded microwave circuits ［J］. IEEE Transactions on Microwave Theory and Techniques, 2006, 54(1): 309-320.
［6］O’BRIEN B, DOOLEY J, BRAZIL T J. RF power amplifier behavioral modeling using a globally recurrent neural network［C］// Proceedings of the 2006 IEEE Microwave Symposium Digest. Piscataway: IEEE, 2006: 1089-1092.
［7］ZHANG C. Behavioral modeling of power amplifier using recurrent neural networks ［D］. Tianjin: Tianjin University, 2014.(张川.递归神经网络对功率放大器的行为级建模［D］. 天津:天津大学,2014.)
［8］ZHANG L, XU J, YAGOUB M, et al. Efficient analytical formulation and sensitivity analysis of neuro-space mapping for nonlinear microwave device modeling［J］. IEEE Transactions on Microwave Theory and Techniques, 2005, 53(9): 2752-2767.
［9］GORISSEN D, ZHANG L, ZHANG Q, et al. Evolutionary neuro-space mapping technique for modeling of nonlinear microwave devices［J］. IEEE Transactions on Microwave Theory and Techniques, 2011, 59(2): 213-229.
［10］ZHANG L, ZHANG Q, WOOD J. Statistical neuro-space mapping technique for large-signal modeling of nonlinear device［J］. IEEE Transactions on Microwave Theory and Techniques, 2008, 54(11): 2453-2467.
［11］ZHANG L, XU J, YAGOUB M, et al. Neuro-space mapping technique for nonlinear device modeling and large-signal simulation［C］// Proceedings of the 2003 IEEE Microwave Symposium Digest. Piscataway: IEEE, 2003: 173-176.
［12］Advanced Design System (ADS): Ver. 2013[CP/OL].[2013-10-10].http://www.keysight.com/zh-CN/pc-1297113/advanced-design-system-ads?nid=-34346.0&cc=CN&lc=chi

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