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Modeling and optimization of microwave filter by ADS ‐based KBNN
Author(s) -
Chen Yi,
Tian Yubo,
Le Mingjun
Publication year - 2017
Publication title -
international journal of rf and microwave computer‐aided engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.335
H-Index - 39
eISSN - 1099-047X
pISSN - 1096-4290
DOI - 10.1002/mmce.21062
Subject(s) - a priori and a posteriori , hfss , particle swarm optimization , artificial neural network , computer science , microwave , software , filter (signal processing) , algorithm , electronic engineering , artificial intelligence , engineering , telecommunications , computer vision , philosophy , microstrip antenna , epistemology , antenna (radio) , programming language
When microwave devices are designed by knowledge‐based neural network (KBNN), the empirical formula is always used as priori knowledge. However, it is difficult to derive the corresponding formulas for the most electromagnetic problems, especially for complex electromagnetic problems, the formula derivation is almost impossible. In this article, they combine neural network with simulation software and use results of Agilent ADS as priori knowledge and HFSS as teaching signal to train the neural network by particle swarm optimization (PSO), which solves the difficulty in obtaining priori knowledge and effectively reduces the complexity of the neural network structure. Based on the KBNN, the microwave filters are designed. The results of optimization satisfy the required specifications which show the effectiveness and superiority of the method.