Surrogate Model based Co-Optimization of Deep Neural Network Hardware Accelerators

2021 ◽  
Author(s):  
Hendrik Wohrle ◽  
Mariela De Lucas Alvarez ◽  
Fabian Schlenke ◽  
Alexander Walsemann ◽  
Michael Karagounis ◽  
...  
Keyword(s):  
Neural Network ◽  
Surrogate Model ◽  
Deep Neural Network ◽  
Hardware Accelerators ◽  
Model Based ◽  
Neural Network Hardware

3-D electromagnetic-model-based absolute attitude estimation using a deep neural network

2021 ◽  
Vol 12 (10) ◽  
pp. 1015-1024
Author(s):  
Xiaoliang Yang ◽  
Weiping Ni ◽  
Weidong Yan ◽  
Hui Bian ◽  
Han Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Attitude Estimation ◽  
Model Based ◽  
Electromagnetic Model

scholarly journals An Ultrashort-Term Net Load Forecasting Model Based on Phase Space Reconstruction and Deep Neural Network

2019 ◽  
Vol 9 (7) ◽  
pp. 1487 ◽  
Author(s):  
Fei Mei ◽  
Qingliang Wu ◽  
Tian Shi ◽  
Jixiang Lu ◽  
Yi Pan ◽  
...  
Keyword(s):  
Neural Network ◽  
Phase Space ◽  
Deep Neural Network ◽  
Time Series Data ◽  
Load Forecasting ◽  
Phase Space Reconstruction ◽  
Series Data ◽  
Forecasting Model ◽  
Model Based ◽  
Net Load

Recently, a large number of distributed photovoltaic (PV) power generations have been connected to the power grid, which resulted in an increased fluctuation of the net load. Therefore, load forecasting has become more difficult. Considering the characteristics of the net load, an ultrashort-term forecasting model based on phase space reconstruction and deep neural network (DNN) is proposed, which can be divided into two steps. First, the phase space reconstruction of the net load time series data is performed using the C-C method. Second, the reconstructed data is fitted by the DNN to obtain the predicted value of the net load. The performance of this model is verified using real data. The accuracy is high in forecasting the net load under high PV penetration rate and different weather conditions.

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