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Received:April 11, 2018 Revised:May 08, 2018
Received:April 11, 2018 Revised:May 08, 2018
中文摘要: 综合考虑影响粮食产量的多种因素，运用改进的粒子群算法优化BP神经网络的初始权重，建立了适合小样本粮食产量的预测模型.实验表明，与BP神经网络粮食预测模型和PSO-BP神经网络粮食预测模型相比，该模型具有更高的预测精度和较大的适应度.
中文关键词: 改进粒子群优化BP神经网络 惯性权重 学习因子 粮食预测模型 预测精度和适应度
Abstract:This study considers comprehensively the various factors of grain production yield and optimizes primary BP neural network weights using the improved Particle Swarm Optimization (PSO) algorithm, then establishes a prediction model suitable for prediction of small sample grain yield. The experiment proves that this model has higher prediction precision and greater fitness than grain yield prediction model based on classical BP neural network and PSO-BP neural network.
keywords: BP neural network optimized by Particle Swarm Optimization (PSO) inertia weight learning factor grain prediction model prediction precision and fitness
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基金项目:国家自然科学基金（11771014）
引用文本：
宗宸生,郑焕霞,王林山.改进粒子群优化BP神经网络粮食产量预测模型.计算机系统应用,2018,27(12):204-209
ZONG Chen-Sheng,ZHENG Huan-Xia,WANG Lin-Shan.Grain Yield Prediction Based on BP Neural Network Optimized by Improved Particle Swarm Optimization.COMPUTER SYSTEMS APPLICATIONS,2018,27(12):204-209
宗宸生,郑焕霞,王林山.改进粒子群优化BP神经网络粮食产量预测模型.计算机系统应用,2018,27(12):204-209
ZONG Chen-Sheng,ZHENG Huan-Xia,WANG Lin-Shan.Grain Yield Prediction Based on BP Neural Network Optimized by Improved Particle Swarm Optimization.COMPUTER SYSTEMS APPLICATIONS,2018,27(12):204-209
