用于微波建模和优化的神经网络和空间映射

Q. Zhang, J. Bandler, S. Koziel, H. Kabir, Lei Zhang
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引用次数: 7

摘要

人工神经网络(ANN)和空间映射被认为是微波计算机辅助设计的两大最新进展。经过训练的人工神经网络可以从元件数据中学习电磁和物理行为,并且训练后的人工神经网络可以用于高级电路设计。空间映射已被证明是工程优化的一个突破,它允许在“粗”模型或替代模型的帮助下有效地执行昂贵的EM优化。最近的进展也导致了神经空间映射,结合了人工神经网络和空间映射的优点,有效地建模微波组件。本文综述了利用人工神经网络、空间映射和神经空间映射进行微波建模和设计的最新进展。
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ANN and space mapping for microwave modelling and optimization
Artificial neural network (ANN) and space mapping are recognized as two major recent advances in microwave CAD. ANNs can be trained to learn EM and physics behaviour from component data, and trained ANNs can be used in high-level circuit design. Space mapping has proved to be a breakthrough in engineering optimization allowing expensive EM optimization to be performed effectively with the help of “coarse” or surrogate models. Recent advance also led to neuro-space mapping, combining the advantages of ANN and space mapping for efficient modelling of microwave components. This paper presents an overview of the state-of-art of microwave modelling and design with ANN, space mapping and neuro-space mapping.
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