Prediction of Grain Yield Using SIGA-BP Neural Network

Niu, Zhixian; Li, Wupeng

doi:10.1007/978-3-642-23881-9_5

Cited by 4 publications

(3 citation statements)

References 1 publication

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“…2011; Niu et al . 2012), and results obtained by the improved model forecast has been found to be more accurate than the original artificial neural network model (Guo et al . 2011).…”

Section: Introductionmentioning

confidence: 99%

“…For example, traditional time series forecast models (Chen & Li 2011;Wang et al 2011) are used to forecast grain production and the results show that the ARIMA model is more suitable for short-term forecasting, typically being based on a prediction horizon of a few hours to a few days (Wang et al 2011). Artificial neural network models and improved models have also been used to forecast grain production (Guo et al 2011;Niu et al 2012), and results obtained by the improved model forecast has been found to be more accurate than the original artificial neural network model (Guo et al 2011). However, the solutions obtained by artificial neural networks fall easily into the local optimum.…”

Section: Introductionmentioning

confidence: 99%

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New optimized grey derivative models for grain production forecasting in China

Liu

Wang

et al. 2014

J. Agric. Sci.

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Although the grey forecasting model has been successfully employed in various fields and demonstrates promising results, the literature shows that its performance could still be improved. Therefore, the aim of the present study was to continue the investigation and derive three hybrid models to predict grain production in China by combining particle swarm optimization (PSO) with the grey linear power index model, the grey logarithm power model and the grey parabola power model. In grey modelling, the use of PSO had the ability to search optimum grey parameters to construct three improved derivative grey models. The results concluded that the improved optimization models with high precision were superior to the traditional models, and PSO contributed more to precision improvement of the three grey models. Furthermore, results from the experiments demonstrated that the optimized models were reliable and valid.

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“…2011; Niu et al . 2012), and results obtained by the improved model forecast has been found to be more accurate than the original artificial neural network model (Guo et al . 2011).…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

New optimized grey derivative models for grain production forecasting in China

Liu

Wang

et al. 2014

J. Agric. Sci.

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show abstract

“…Chen (2011) predicted the unit grain yield of Hunan Province by using weighted Markov chain model, regression model and grey system model. Niu (2012) presented an Adaptive Immune Genetic Algorithm optimization to optimize BP neural network's initial weights based on the concentration of the immune system regulation mechanism and Genetic Algorithm global optimization characteristics, for the BP neural network prediction of grain production fall into local optimum easily. Xiang and Zhang (2013) proposed a grain yield prediction model based on grey theory and markov model and the simulation results show that the proposed algorithm has better prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

The Combination Forecasting Model of Grain Production Based on Stepwise Regression Method and RBF Neural Network

Yang¹,

Li²

2015

AJFST

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In order to improve the accuracy of grain production forecasting, this study proposed a new combination forecasting model, the model combined stepwise regression method with RBF neural network by assigning proper weights using inverse variance method. By comparing different criteria, the result indicates that the combination forecasting model is superior to other models. The performance of the models is measured using three types of error measurement, which are Mean Absolute Percentage Error (MAPE), Theil Inequality Coefficient (Theil IC) and Root Mean Squared Error (RMSE). The model with smallest value of MAPE, Theil IC and RMSE stands out to be the best model in predicting the grain production. Based on the MAPE, Theil IC and RMSE evaluation criteria, the combination model can reduce the forecasting error and has high prediction accuracy in grain production forecasting, making the decision more scientific and rational.

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GS-SVR: Analysis and Prediction of Henan Province Grain Production Using Support Vector Regression

Wei

Huang

2021

2021 33rd Chinese Control and Decision Conference (CCDC)

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Prediction of Grain Yield Using SIGA-BP Neural Network

Cited by 4 publications

References 1 publication

New optimized grey derivative models for grain production forecasting in China

New optimized grey derivative models for grain production forecasting in China

The Combination Forecasting Model of Grain Production Based on Stepwise Regression Method and RBF Neural Network

GS-SVR: Analysis and Prediction of Henan Province Grain Production Using Support Vector Regression

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