The Research for Experiment and Elman Neural Network Model of High Manganese Steel Drilling Power

2010 ◽  
Author(s):  
Yang Liang ◽  
Yan Changgang ◽  
Li Xu
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Manganese Steel ◽  
High Manganese ◽  
High Manganese Steel ◽  
Elman Neural Network

scholarly journals Research on Life Prediction of IGBT Devices Based on Elman Neural Network Model

2020 ◽  
Vol 1550 ◽  
pp. 032148
Author(s):  
Chao Xing ◽  
Xinze Xi ◽  
Xin He ◽  
Shengnan Li ◽  
Mingqun Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Life Prediction ◽  
Elman Neural Network

scholarly journals Runoff Prediction Method Based on Adaptive Elman Neural Network

Water ◽  
2019 ◽  
Vol 11 (6) ◽  
pp. 1113 ◽  
Author(s):  
Chenming Li ◽  
Lei Zhu ◽  
Zhiyao He ◽  
Hongmin Gao ◽  
Yao Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Mutual Information ◽  
Network Model ◽  
Neural Network Model ◽  
Prediction Method ◽  
Elman Neural Network ◽  
Runoff Forecasting ◽  
Runoff Prediction ◽  
Screening Factor

The prediction of medium- and long-term runoff is of great significance to the comprehensive utilization of water resources. Building an adaptive data-driven runoff prediction model by automatic identification of multivariate time series change in runoff forecasting and identifying its influence degree is an attractive and intricate task. At present, the commonly used screening factor method is correlational analysis; others offer multi-collinearity. If these factors are directly input into the model, the parameters of the model tend to increase, and the excessive redundancy and noise adversely affects the prediction results of the model. On the basis of previous studies on medium- and long-term runoff prediction methods, this paper proposes an Elman Neural Network (ENN) adaptive runoff prediction method based on normalized mutual information (NMI) and kernel principal component analysis (KPCA). In this method, the features of the screening factors are extracted automatically by using the mutual information automatic screening factor, and then input into the Elman Neural Network for training. With less features, the parameters of the Elman Neural Network model can be reduced, and the problem of overfitting of the Elman Neural Network model is effectively alleviated. The method is evaluated by using the annual average runoff data of Jinping hydropower station in Chengdu, China, from 2007 to 2011. The maximum relative error of multiple forecasts was found to be less than 16%, and forecast effect was good. The accuracy of prediction is further improved by averaging the results of multiple forecasts.

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