A Short-Term Prediction Method of Shanghai Composite Index Based on Back Propagation Neural Network and Inheritance Algorithm

2008 ◽  
Author(s):  
Shen Wei ◽  
Yin Lu ◽  
Wang Yongli
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Composite Index ◽  
Prediction Method ◽  
Back Propagation Neural Network ◽  
Short Term ◽  
Term Prediction ◽  
Short Term Prediction

Short-Term Prediction Model for a Grid-Connected Photovoltaic System Using EMD and GABPNN

2013 ◽  
Vol 291-294 ◽  
pp. 74-82
Author(s):  
Zeng Wei Zheng ◽  
Yuan Yi Chen ◽  
Xiao Wei Zhou ◽  
Mei Mei Huo ◽  
Bo Zhao ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Prediction Model ◽  
Prediction Method ◽  
Photovoltaic System ◽  
Photovoltaic Systems ◽  
Short Term ◽  
Model Based ◽  
Term Prediction ◽  
Short Term Prediction

The integration between photovoltaic systems and tradition grid have a lot of challenges. To accurately predict is a key to solve these challenges. Due to complex, non-linear and non-stationary characteristics, it is difficult to accurately predict the power of photovoltaic systems. In this paper, a short-term prediction model based on empirical mode decomposition (EMD)and back propagation neural network(BPNN) was constructed, and use genetic algorithm as the learn algorithm of BPNN. The power data after pre-processing is decomposed into several components, then using prediction model based on BPNN and genetic algorithm to predict each component, and all the component prediction values were aggregated to obtain the ultimate predicted result. The simulation shows the purposed prediction model has higher prediction precision compare with traditional neural network prediction method and it is an effective prediction method of photovoltaic systems.

scholarly journals Stock Market Prediction Using Artificial Neural Networks

2012 ◽  
Vol 6-7 ◽  
pp. 1055-1060 ◽  
Author(s):  
Yang Bing ◽  
Jian Kun Hao ◽  
Si Chang Zhang
Keyword(s):  
Neural Network ◽  
Prediction Models ◽  
Learning Algorithm ◽  
Stock Exchange ◽  
Back Propagation ◽  
Composite Index ◽  
Network Models ◽  
Back Propagation Neural Network ◽  
Neural Network Models ◽  
The Neural Network

In this study we apply back propagation Neural Network models to predict the daily Shanghai Stock Exchange Composite Index. The learning algorithm and gradient search technique are constructed in the models. We evaluate the prediction models and conclude that the Shanghai Stock Exchange Composite Index is predictable in the short term. Empirical study shows that the Neural Network models is successfully applied to predict the daily highest, lowest, and closing value of the Shanghai Stock Exchange Composite Index, but it can not predict the return rate of the Shanghai Stock Exchange Composite Index in short terms.

Short Term Electric Load Forecasting with Back Propagation Neural Network and Simulated Annealing Genetic Algorithm

2015 ◽  
Vol 785 ◽  
pp. 14-18 ◽  
Author(s):  
Badar ul Islam ◽  
Zuhairi Baharudin ◽  
Perumal Nallagownden
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Hybrid Approach ◽  
Back Propagation ◽  
Forecast Accuracy ◽  
Back Propagation Neural Network ◽  
Search Performance ◽  
Training Algorithm ◽  
Short Term

Although, Back Propagation Neural Network are frequently implemented to forecast short-term electricity load, however, this training algorithm is criticized for its slow and improper convergence and poor generalization. There is a great need to explore the techniques that can overcome the above mentioned limitations to improve the forecast accuracy. In this paper, an improved BP neural network training algorithm is proposed that hybridizes simulated annealing and genetic algorithm (SA-GA). This hybrid approach leads to the integration of powerful local search capability of simulated annealing and near accurate global search performance of genetic algorithm. The proposed technique has shown better results in terms of load forecast accuracy and faster convergence. ISO New England data for the period of five years is employed to develop a case study that validates the efficacy of the proposed technique.

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