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Modelling and optimization of multilayer RF passives using coupled neural networks and genetic algorithms

机译:使用耦合神经网络和遗传算法的多层射频射频的建模与优化

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We present the first combined neural network and genetic algorithm based modelling, design and optimization of multilayer passives. The embedded multilayer passives considered are of great interest for RF system-on-package for W-CDMA and C-band applications. An accurate neural network model for multilayer inductors and capacitors is developed using measured results for the frequency range of 1-5 GHz. The neural network model is used to perform sensitivity analysis and derive response surfaces. An innovative technique is then applied in which genetic algorithm based optimization is coupled with neural network modelling of electrical performance. The proposed neuro-genetic algorithm based design promises to minimize the time and cost for multilayer passive design while providing greater accuracy.
机译:我们介绍了多层自由度的​​基于综合神经网络和基于遗传算法的建模,设计和优化。对于W-CDMA和C波段应用程序的RF系统,嵌入式多层被动的激活者是非常兴趣的。使用测量结果为1-5 GHz的频率范围的测量结果开发了一种用于多层电感器和电容器的准确的神经网络模型。神经网络模型用于执行灵敏度分析和派生响应表面。然后应用一种创新的技术,其中基于遗传算法的优化与电气性能的神经网络建模相结合。所提出的基于神经遗传算法的基于设计的设计有望最大限度地减少多层被动设计的时间和成本,同时提供更高的准确性。

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