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首页> 外文会议>IFIP TC 12 conference on computer and computing technologies in agriculture;CCTA 2010 >Prediction of Agricultural Machinery Total Power Based on PSO-GM(2,1, λ, ss) Model
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Prediction of Agricultural Machinery Total Power Based on PSO-GM(2,1, λ, ss) Model

机译：基于PSO-GM（2,1，λ，ss）模型的农机总动力预测

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In order to improve the prediction accuracy of agricultural machinery total power then to provide the basis for the agricultural mechanization development goals, the paper used gray GM(2,1) model in the prediction. Through the introduction of parameter X to correct the background value and parameter p for multiple transformation on the initial data, the model was expanded to GM(2,l,λ,β) model and prediction accuracy was improved. Because of the nonlinear traits between parameter X, p and the prediction errors, they are difficult to be solved. The paper used Particle Swarm Optimization (PSO) to search the best parameter X,p , then combination forecast model of PSO-GM(2,l,λ,β) was constructed. In order to avoid incorrect selection of inertia weight w causing the global search and local search imbalance, the paper used Decreasing Inertia Weight Particle Swarm Optimization, in which parameter w gradually decreases from 1.4 to 0.35. And agricultural machinery total power was predicted based on Zhejiang province's statistics. Predicted results show that the combination forecast model prediction accuracy is higher than the gray GM(1,1) model and the model better fits the data. The forecast of the agricultural machinery total power of this combination forecast model is feasible and effective, and should be feasible in other areas of agriculture prediction.
机译：为了提高农机总动力的预测精度，为农机化发展目标提供依据，在预测中采用了灰色GM（2,1）模型。通过引入参数X来校正背景值和参数p以便对初始数据进行多次转换，将模型扩展为GM（2，l，λ，β）模型，并提高了预测精度。由于参数X，p与预测误差之间存在非线性特性，因此很难解决。本文采用粒子群算法（PSO）搜索最佳参数X，p，然后建立PSO-GM（2，l，λ，β）的组合预测模型。为了避免错误选择惯性权重w导致全局搜索和局部搜索不平衡，本文使用了减小惯性权重粒子群优化算法，其中参数w从1.4逐渐减小到0.35。根据浙江省的统计数据预测了农业机械的总功率。预测结果表明，组合预测模型的预测精度高于灰色GM（1,1）模型，并且该模型更好地拟合了数据。该组合预测模型对农机总动力的预测是可行和有效的，在其他农业预测领域应该是可行的。

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