Lopez-Pacheco Mario, Yu Wen
Departamento de Control Automático, CINVESTAV-IPN, Ciudad de México, Mexico.
Neural Process Lett. 2022;54(1):559-580. doi: 10.1007/s11063-021-10644-1. Epub 2021 Sep 23.
Deep learning models, such as convolutional neural networks (CNN), have been successfully applied in pattern recognition and system identification recent years. But for the cases of missing data and big noises, CNN does not work well for dynamic system modeling. In this paper, complex valued convolution neural network (CVCNN) is presented for modeling nonlinear systems with large uncertainties. Novel training methods are proposed for CVCNN. Comparisons with other classical neural networks are made to show the advantages of the proposed methods.
近年来，深度学习模型，如卷积神经网络（CNN），已成功应用于模式识别和系统识别。但对于数据缺失和噪声较大的情况，CNN在动态系统建模方面效果不佳。本文提出了复值卷积神经网络（CVCNN）用于对具有较大不确定性的非线性系统进行建模。针对CVCNN提出了新颖的训练方法。通过与其他经典神经网络进行比较，展示了所提方法的优势。
