A hybrid adaptive time-delay neural network model for multi-step-ahead prediction of sunspot activity

2006 ◽  
Vol 28 (3/4) ◽  
pp. 364 ◽  
Author(s):  
Jing Xin Xie ◽  
Chun Tian Cheng ◽  
Kwok Wing Chau ◽  
Yong Zhen Pei
Keyword(s):  
Neural Network ◽  
Time Delay ◽  
Network Model ◽  
Neural Network Model ◽  
Sunspot Activity ◽  
Adaptive Time

scholarly journals Prediction Model of Hot Metal Silicon Content Based on Improved GA-BPNN

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Zeqian Cui ◽  
Yang Han ◽  
Chaomeng Lu ◽  
Yafeng Wu ◽  
Mansheng Chu
Keyword(s):  
Neural Network ◽  
Blast Furnace ◽  
Time Delay ◽  
Network Model ◽  
Neural Network Model ◽  
Silicon Content ◽  
Large Time ◽  
Hot Metal ◽  
Multiple Control ◽  
Data Set

The inconsistency of the detection period of blast furnace data and the large time delay of key parameters make the prediction of the hot metal silicon content face huge challenges. Aiming at the problem that the hot metal silicon content is not consistent with the detection period of time series of multiple control parameters, the cubic spline interpolation fitting model was used to realize the data integration of multiple detection periods. The large time delay of the blast furnace iron making process was analyzed. Moreover, Spearman analysis was combined with the weighted moving average method to optimize the data set of silicon content prediction. Aiming at the problem of low prediction accuracy of the ordinary neural network model, genetic algorithm was used to optimize parameters on the BP neural network model to improve the convergence speed of the model to achieve global optimization. Combined with the autocorrelation analysis of the hot metal silicon content, a modified model for the prediction of hot metal silicon content based on error analysis was proposed to further improve the accuracy of the prediction. The model comprehensively considers problems such as data detection inconsistency, large time delay, and inaccuracy of prediction results. Its average absolute error is 0.05009, which can be used in actual production.

A Time Delay Neural Network model for simulating eye gaze data

2011 ◽  
Vol 23 (1) ◽  
pp. 111-126 ◽  
Author(s):  
Yun Zhang ◽  
Xiaoyu Zhao ◽  
Hong Fu ◽  
Zhen Liang ◽  
Zheru Chi ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Delay ◽  
Network Model ◽  
Neural Network Model ◽  
Eye Gaze
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