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- W2348121231 abstract "With respect to the prediction of volatile and non-stationary load sequence,this paper established a gray neural network prediction model based on the Kalman filtering and particle swarm optimization algorithm( R. E Kalman-G( 1,1)-PSO-BP). The model uses the Kalman filtering algorithm to eliminate random errors from the non-stationary sequence and obtain useful information close to the real situation,so that the load measurement sequence is filtered. The measurement sequence after such filtering undergoes fitting prediction according to the GM( 1,1) model algorithm. The residual error is corrected in the BP neural network algorithm based on the particle swarm optimization to obtain a new predictive value. Practice shows that the overall accuracy of the new predictive value is much higher than that of GM( 1,1) model and Kalman-G( 1,1) model. Thus,the model established in this paper has a high value in use." @default.
- W2348121231 created "2016-06-24" @default.
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- W2348121231 date "2014-01-01" @default.
- W2348121231 modified "2023-09-23" @default.
- W2348121231 title "Gray Neural Network Prediction Model Based on Kalman Filtering and Particle Swarm Optimization Algorithm" @default.
- W2348121231 hasPublicationYear "2014" @default.
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