Water Pollution Forecasting Model of the Back-Propagation Neural Network Based on One Step Secant Algorithm

2010 ◽  
pp. 458-464 ◽  
Author(s):  
Xiaoyun Yue ◽  
Yajun Guo ◽  
Jinran Wang ◽  
Xuezhi Mao ◽  
Xiaoqing Lei
Keyword(s):  
Neural Network ◽  
Water Pollution ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Forecasting Model ◽  
One Step

One-Step Weights Updating Back-Propagation Neural Network for Nonlinear System Identification

2015 ◽  
Vol 734 ◽  
pp. 558-561
Author(s):  
Yang Yang
Keyword(s):  
Neural Network ◽  
System Identification ◽  
Nonlinear System ◽  
Nonlinear System Identification ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Recursive Least Squares ◽  
Identification Performance ◽  
High Efficient ◽  
One Step

Given that many filters such as least mean squares and recursive least squares which are not able to deal with nonlinear system. In this paper, a nonlinear system identification technique using a specially designed neural network is investigated. Precisely, a power-activated back-propagation neural network is first constructed. Then, a high efficient weights updating method which only requires one-step iteration in its training session is presented. The system identification performance is evaluated through MATLAB simulations. The simulation results validate the one-step weights updating method and show satisfactory nonlinear system identification performance.

A Robust Algorithm for Wind Power Forecasting Based on Projection Pursuit and Back Propagation Neural Network

2013 ◽  
Vol 291-294 ◽  
pp. 429-434
Author(s):  
Wen Xia Liu ◽  
Ying Zhi Li
Keyword(s):  
Neural Network ◽  
Principal Components ◽  
Wind Farm ◽  
Back Propagation ◽  
Projection Pursuit ◽  
Back Propagation Neural Network ◽  
Forecasting Model ◽  
Absolute Deviation ◽  
Index Function ◽  
Trained Network

This paper has proposed a wind farm generation output forecasting model based on projection pursuit (PP) and back propagation neural network (BPNN), in order to eliminate the influence of the bad points and mutations on and enhance robustness of the forecasting model. A median absolute deviation is used as projection index function, effectively avoiding the influence of the outlier. Firstly, Extract the principal components of each factor by PP. Then, input the principal components to the BPNN for training the network. Finally, forecast the wind farm generation output via the trained network. The simulation shows that the proposed approach is of higher accuracy.

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