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• W2895858327 abstract "Randomized algorithms have been successfully applied in modeling dynamic system. How do random weights affect system identification and why do they sometimes work well? In this paper, we use the convolutional neural network (CNN) as an identification model to answer these questions. Since the convolution operation is an important property of the dynamic system and in the frequency domain it becomes the product, the CNN model is analyzed in the frequency domain. We first modify the CNN model, so that it can model both the input and the output series. Then we analyze the impact of the random weights of CNN in the frequency domain. We prove the existence of optimal weights and analyze the modeling accuracy under optimal weights and random weights. Through theoretical analysis, we propose a two-step training method and compare it with the random weight algorithm. The proposed CNN model with random weights is validated with three benchmark problems." @default.
• W2895858327 created "2018-10-26" @default.
• W2895858327 creator A5008201587 @default.
• W2895858327 creator A5066451304 @default.
• W2895858327 date "2019-03-01" @default.
• W2895858327 modified "2023-10-05" @default.
• W2895858327 title "Impact of random weights on nonlinear system identification using convolutional neural networks" @default.
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• W2895858327 doi "https://doi.org/10.1016/j.ins.2018.10.019" @default.
• W2895858327 hasPublicationYear "2019" @default.
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