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- W2367965506 abstract "Accurate prediction of grain yield has a great significance for food safety and social stability.An improved Back-propagation Neural Network(BPNN) model named REMCC-BPNN for time series forecasting was proposed.REMCC-BPNN optimizes the training model for BPNN based on the minimum correlation coefficient of the absolute value of the K nearest neighbor training samples' fitting relative error and the K training samples' time order.Two real-world datasets,the grain yield from 1985 to 2011 in China and the grain yield from 1995 to 2010 in Hunan,China,was used to test the effectiveness of REMCC-BPNN.The results showed that the prediction accuracy of REMCC-BPNN is better than that of several frequently-used prediction models for time series,such as BPNN,SVR,ARIMA and GM(1,N).The REMCC-BPNN prediction model is faster and more stable." @default.
- W2367965506 created "2016-06-24" @default.
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- W2367965506 date "2013-01-01" @default.
- W2367965506 modified "2023-09-27" @default.
- W2367965506 title "Study on Prediction for Grain Yield Based on REMCC-BPNN" @default.
- W2367965506 hasPublicationYear "2013" @default.
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