微波食品加热
人工神经网络
计算机科学
滤波器(信号处理)
电子工程
人工智能
电信
工程类
计算机视觉
作者
Leyu Bi,Weihua Cao,Wenkai Hu,Yan Yuan,Min Wu
标识
DOI:10.1016/j.ifacol.2020.12.117
摘要
Abstract In performance tuning of many electromechanical devices, well-trained operators are in great demand. However, manual tuning is costly and time-consuming, and thus do not conform to the trend of smart manufacturing. Microwave filters are typical electromechanical devices. Their tuning performance is limited by low extraction accuracy and high dimensionality of circuit features. In this paper, a hybrid modeling method based on neural networks is proposed to get better tuning performance. First, a curve-shape-based modeling method using Convolutional Neural Networks is presented to bypass the cumbersome extraction of circuit features. Second, a multi-model optimized fusion model based on Elman Neural Networks is constructed to cope with the high-dimensional property of circuit features, and improve modeling accuracy. The effectiveness of the hybrid modeling method is demonstrated through experiments. It achieves better tuning performance with fewer samples compared with two single modeling methods.
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