| 马涛，王芬.基于CPSO-SVM 的小麦条锈病预测模型研究[J].广东农业科学,2014,41(17):74-78 |
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基于CPSO-SVM 的小麦条锈病预测模型研究 |
| Research on prediction model of wheat stripe rust based on CPSO-SVM |
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| DOI： |
| 中文关键词: 混沌粒子群算法 支持向量机 小麦条锈病 预测模型 |
| 英文关键词: Chaotic particle swarm optimization algorithm support vector machine wheat stripe rust prediction model |
| 基金项目:宁夏自然科学基金(NZ132014)；宁夏高等学校科研项目（NGY201306）；宁夏师范学院项目(ZD201311，YB201434,YB201439)；宁夏大学生创新创业计划项目(NJ201234815)；宁夏师范学院创新团队项目(PY201208)；宁夏2012 年度高等学校教育教学改革项目 |
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| 摘要点击次数: 1225 |
| 全文下载次数: 684 |
| 中文摘要: |
| 针对BP 神经网络在建立模型时没有确定网络结构的缺点和支持向量机(SVM)模型参数选择对预测精度影响大的局限性，提出一种结合混沌系统的粒子群算法(CPSO)去优化SVM 模型的惩罚因子C 和核函数中参数σ的混合模型，利用混沌系统的不确定性理论使传统的粒子群算法能有效克服收敛速度慢、容易达到局部最优值的缺点，使CPSO 算法能更快、更准确找到全局最优值。经过参数优化SVM 模型有效提高了预测精度并利用新的混合模
型对宁夏地区小麦条锈病流行级别进行预测。结果表明，相对于传统SVM 模型、组合PSO-SVM 模型和组合PSO-BP神经网络模型，研究提出的混合模型(CPSO-SVM)在预测精度及模型的泛化能力上均优于以上3 种模型，可对小麦条锈病发病及流行程度进行精确的预测，并为农业病虫害研究部门提供有效的理论依据。 |
| 英文摘要: |
| In the view of disadvantages of uncertain network structure in the BP neural network to establish model and big influence of different parameters selection on prediction accuracy for SVM model, a novel combining model was proposed. The parameters of penalty parameters C and σof the SVM model were optimized by particle swarm optimization algorithm with the chaos system (CPSO). Utilizing the uncertainty of chaotic systems, the shortcomings of slow convergence speed and easy to find the local optimal value were conquered by traditional PSO algorithm. In this way, the global optimal value could be faster and more accuracy to find with the novel approach of CPSO. The SVM model with parameter optimization effectively improved the prediction accuracy. The popular class of wheat stripe rust in Ningxia was predicted with proposed novel approach (CPSO-SVM). The results showed that, compared with the SVM model, PSO-SVM model and PSO -BP neural network model, the combining model were better than the above three kinds of models in prediction accuracy and generalization capability. The prevalence levels of wheat stripe rust can be accuracy predicted by the CPSOSVM algorithm and it can provide effective theory for agricultural pests and diseases research department. |
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