水稻虫害智能预测模型及其应用
文章导航 > 农业工程学报 > 2008 > 24(7) : 141-145.
汪 璇, 吕家恪, 胡小梅, 谢德体. 水稻虫害智能预测模型及其应用[J]. 农业工程学报, 2008, 24(7): 141-145.
引用本文: 汪 璇, 吕家恪, 胡小梅, 谢德体. 水稻虫害智能预测模型及其应用[J]. 农业工程学报, 2008, 24(7): 141-145.
Wang Xuan, Lü Jiake, Hu Xiaomei, Xie Deti. Intelligent prediction model for rice pests and its application[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2008, 24(7): 141-145.
Citation: Wang Xuan, Lü Jiake, Hu Xiaomei, Xie Deti. Intelligent prediction model for rice pests and its application[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2008, 24(7): 141-145.
水稻虫害智能预测模型及其应用
1.西南大学计算机与信息科学学院，重庆 400716
2.重庆市数字农业重点实验室，重庆 400716
3.四川大学生命科学学院，成都 610065
基金项目: 重庆市自然科学基金项目（8021）资助
计量 文章访问数: 1426 HTML全文浏览量: 2 PDF下载量: 992 出版历程 收稿日期: 2006-05-23 修回日期: 2007-11-18 发布日期: 2008-07-31Intelligent prediction model for rice pests and its application
1.College of Computer and Information Science, Southwest University, Chongqing 400716, China
2.Key Laboratory of Chongqing Digital Agriculture, Chongqing 400716, China
3.College of Biology Science, Sichuan University, Chengdu 610065, China
摘要
摘要: 为改进受多变量、时变和不确定因素影响的作物虫情预测的效率和准确性，将人工神经网络、遗传算法和模拟退火技术相结合，提出了一个全新的水稻虫害智能预测模型。模型首先基于人工神经网络，利用现有的多维气象数据、虫害历史数据构建网络结构，然后将遗传算法置于网络内层，模拟退火算法置于网络外层，对神经网络权重和阈值进行优化训练，以使模型输出快速准确地逼进目标样本。模型被应用在重庆市永川水稻二化螟虫情预测中，结果表明该模型能够较精确地预测未来虫害的发生程度。与传统的BP人工神经网络预测相比，预测精度和预测时间都得到较大提高，因而利用智能模型进行水稻虫害预测具有良好的实用价值。
Abstract: Prediction of rice pests is greatly influenced by uncertain factors, for example time and weather change. To improve prediction efficiency and precision, a new intelligent prediction model for rice pests, which uses artificial neural network, genetic algorithm and simulated annealing algorithm is proposed. First the network model structure was built based on artificial neural network technology, and available multi-dimensional data as well as pest occurrence levels history data were used as network input and output variables. Then genetic algorithm was set in network exterior circle, and simulated annealing algorithm was applied in network interior circle to train network nodes connections weights and thresholds until model output results approximate target vector. The model was applied to Chongqing Yongchuan rice chilo suppressalis predictions. Result shows that model can predict future chilo suppressalis occurrence level more exactly and compared with traditional BP neuron network, model prediction accuracy and operation time were improved greatly. So for rice pests prediction, computational intelligence technology is of good practical utility value.
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计量 文章访问数: 1426 HTML全文浏览量: 2 PDF下载量: 992 出版历程 收稿日期: 2006-05-23 修回日期: 2007-11-18 发布日期: 2008-07-31目录
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