2021
DOI: 10.1016/j.procir.2020.05.249
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Prediction of the dynamic performance for the deployable mechanism in assembly based on optimized neural network

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Cited by 8 publications
(5 citation statements)
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“…Another method is to use extended convolution. Compared with general convolution, in addition to the size of the convolution kernel, the dilation rate used to represent the size of the extended convolution is increased [16].…”
Section: Extended Convolutionmentioning
confidence: 99%
See 1 more Smart Citation
“…Another method is to use extended convolution. Compared with general convolution, in addition to the size of the convolution kernel, the dilation rate used to represent the size of the extended convolution is increased [16].…”
Section: Extended Convolutionmentioning
confidence: 99%
“…Considering that the traffic flow has a weekly periodicity, the traffic flow at the current moment is similar to that at the same time last week, and the difference is used to subtract the traffic flow at the same time last week from the traffic flow at the current moment. (2) LSTM1 and LSTM2 represent single-layer LSTM and double-layer LSTM, respectively, and the number of hidden layer units is 64; (3) GRU1 and GRU2 indicate that single-layer GRU and double-layer GRU are used, respectively, and the number of hidden layer units is 64; (4) e expansion coefficient of each layer of DCFCN is [1,2,4,8,16,32], the number of convolutional kernels of each layer is 32, and the size of convolutional kernels is 4. As can be seen from Table 2, compared with other comparison models, the proposed DCFCN has the best prediction effect and the lowest in RMSE, MAE, and MAPE indicators.…”
Section: Experimental Settingsmentioning
confidence: 99%
“…Examples encompass numerical algorithms analyzing the final unfolding angles of adjusted joints in a linkage, neural networks predicting final errors for achieving higher accuracy, and adaptive support vector machines (ASVMs) based on the SVM framework predicting the assembly quality of automotive sunroofs. Alternative approaches include using artificial intelligence techniques to narrow down the search space for assembly sequence planning, considering an analysis of the limitations of existing methods [20][21][22]. Leveraging the potent fitting capability of machine learning, these methods can yield superior results compared to traditional algorithms.…”
Section: Introductionmentioning
confidence: 99%
“…Ding et al (2020) studied the deployable mechanism of cable winding device and proposed a novel solution to improve the accuracy of the assembly. Yu et al (2021) present an efficient assembly optimizer of the deployable mechanism based on machine learning and predict the dynamic performance through the neural network to improve the assembly accuracy.…”
Section: Introductionmentioning
confidence: 99%