The development of accurate prediction models continues to be highly beneficial in myriad disciplines. Deep learning models have performed well in stock price prediction and give high accuracy. However, these models a...The development of accurate prediction models continues to be highly beneficial in myriad disciplines. Deep learning models have performed well in stock price prediction and give high accuracy. However, these models are largely affected by the vanishing gradient problem escalated by some activation functions. This study proposes the use of the Vanishing Gradient Resilient Optimized Gated Recurrent Unit (OGRU) model with a scaled mean Approximation Coefficient (AC) time lag which should counter slow convergence, vanishing gradient and large error metrics. This study employed the Rectified Linear Unit (ReLU), Hyperbolic Tangent (Tanh), Sigmoid and Exponential Linear Unit (ELU) activation functions. Real-life datasets including the daily Apple and 5-minute Netflix closing stock prices were used, and they were decomposed using the Stationary Wavelet Transform (SWT). The decomposed series formed a decomposed data model which was compared to an undecomposed data model with similar hyperparameters and different default lags. The Apple daily dataset performed well with a Default_1 lag, using an undecomposed data model and the ReLU, attaining 0.01312, 0.00854 and 3.67 minutes for RMSE, MAE and runtime. The Netflix data performed best with the MeanAC_42 lag, using decomposed data model and the ELU achieving 0.00620, 0.00487 and 3.01 minutes for the same metrics.展开更多
The relationship between A1GaN/GaN HEMT gate field plate （FP） and surface-state-related gate lag phenomena is investigated by two-dimensional numerical transient simulations to study the mechanism of the influence o...The relationship between A1GaN/GaN HEMT gate field plate （FP） and surface-state-related gate lag phenomena is investigated by two-dimensional numerical transient simulations to study the mechanism of the influence of FPs on current collapse. The simulations reveal that adding a field plate has a noticeable impact on the extent of current collapse while it has no influence on lapsed time. The FP is found to suppress current collapse through reducing the ionization probability of surface states by enhancing free hole accumulation next to the AIGaN surface between gate and drain.展开更多
文摘The development of accurate prediction models continues to be highly beneficial in myriad disciplines. Deep learning models have performed well in stock price prediction and give high accuracy. However, these models are largely affected by the vanishing gradient problem escalated by some activation functions. This study proposes the use of the Vanishing Gradient Resilient Optimized Gated Recurrent Unit (OGRU) model with a scaled mean Approximation Coefficient (AC) time lag which should counter slow convergence, vanishing gradient and large error metrics. This study employed the Rectified Linear Unit (ReLU), Hyperbolic Tangent (Tanh), Sigmoid and Exponential Linear Unit (ELU) activation functions. Real-life datasets including the daily Apple and 5-minute Netflix closing stock prices were used, and they were decomposed using the Stationary Wavelet Transform (SWT). The decomposed series formed a decomposed data model which was compared to an undecomposed data model with similar hyperparameters and different default lags. The Apple daily dataset performed well with a Default_1 lag, using an undecomposed data model and the ReLU, attaining 0.01312, 0.00854 and 3.67 minutes for RMSE, MAE and runtime. The Netflix data performed best with the MeanAC_42 lag, using decomposed data model and the ELU achieving 0.00620, 0.00487 and 3.01 minutes for the same metrics.
文摘The relationship between A1GaN/GaN HEMT gate field plate （FP） and surface-state-related gate lag phenomena is investigated by two-dimensional numerical transient simulations to study the mechanism of the influence of FPs on current collapse. The simulations reveal that adding a field plate has a noticeable impact on the extent of current collapse while it has no influence on lapsed time. The FP is found to suppress current collapse through reducing the ionization probability of surface states by enhancing free hole accumulation next to the AIGaN surface between gate and drain.
