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基于神經(jīng)網(wǎng)絡(luò)的S參數(shù)估計法

周力,  毛鈞杰,  姚德淼

周力, 毛鈞杰, 姚德淼. 基于神經(jīng)網(wǎng)絡(luò)的S參數(shù)估計法[J]. 電子與信息學(xué)報, 2001, 23(3): 304-307.
引用本文: 周力, 毛鈞杰, 姚德淼. 基于神經(jīng)網(wǎng)絡(luò)的S參數(shù)估計法[J]. 電子與信息學(xué)報, 2001, 23(3): 304-307.
Zhou Li, Mao Junjie, Yao Demiao. A Method Based on Neural Network to Estimate Scattering Parameter[J]. Journal of Electronics & Information Technology, 2001, 23(3): 304-307.
Citation: Zhou Li, Mao Junjie, Yao Demiao. A Method Based on Neural Network to Estimate Scattering Parameter[J]. Journal of Electronics & Information Technology, 2001, 23(3): 304-307.

基于神經(jīng)網(wǎng)絡(luò)的S參數(shù)估計法

A Method Based on Neural Network to Estimate Scattering Parameter

  • 摘要: 該文討論了神經(jīng)網(wǎng)絡(luò)用于微波電路的一種新的訓(xùn)練算法,是基于誤差反傳算法與模擬退火算法相結(jié)合的。本算法可以解決BP網(wǎng)的局部最小問題。并且不用對神經(jīng)網(wǎng)絡(luò)模型的結(jié)構(gòu)作任何改動。通過一個具體算例的計算驗證了這種算法。
    Abstract: In this paper, a new neural network s training method combining back error prop-agation with simulated annealing used to microwave circuit design is discussed. This method can solve the local minimum question of the BP neural network. The new training technique does not require any structure change in neural network model. An example is calculated and. the results verified the proposed technique.
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  • M. Vai, S. Prasad, Automatic impedance matching with a neural network. IEEE Microwave Guided Wave Lett., 1993, 3(3), 353-354.[2]P.M. Watson, K. C. Gupta, Design and optimizasion of CPW circuits, IEEE Trans. on MTT,1997, 45(12), 2515-2523.[3]B.G. Hoskins, M. R. Haskard, Artificial neural network techniques for the estimation of thick-film resistance, Microelectronics Journal, 1995, 25(1), 9-16.[4]A.H. Zaabab, Zhang Q. J, M. Nakhla, A neural network modeling approach to circuits optimization and statistical design, IEEE Trans. on MTT, 1995, 43(6), 1349-1358.[5]A. Veluswami, M. S. Nakhla, Zhang Q. J, The application of neural networks to EM-base simulation and optimization of interconnects in high-speed VLSI circuits, IEEE Trans. on MTT, 1997,45(5), 712-722.[6]A.H. Zaabab, Zhang Q. J, M. S. Nakhla, Device and circuit-level modeling using neural networks with faster training base on network sparsity, IEEE Trans. on MTT, 1997, 45(10), 1696-1704.[7]P.M. Watson, K. G. Gupta, EMM-ANN models for microstrip via and interconnects in multilayer circuits, IEEE Trans. on MTT, 1997, 45(12), 2495-2503.[8]F.F. Wang, Zhang Q J, Knowledge-based neural models for microwave design, IEEE Trans. on MTT, 1997,45(12), 2333-2343.[9]K.L. Finch, N. Alexopoulos, Shunt post in microstrip transmission lines, IEEE Trans. on MTT,1990, 38(11), 1585-1594.
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出版歷程
  • 收稿日期:  1999-04-14
  • 修回日期:  1999-10-25
  • 刊出日期:  2001-03-19

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