不同优化方法的混沌- RBF神经网络模型对大白菜短期价格预测的结果比较

崔利国,李哲敏

系统科学与数学 ›› 2013, Vol. 33 ›› Issue (1) : 45-54.

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系统科学与数学 ›› 2013, Vol. 33 ›› Issue (1) : 45-54. DOI: 10.12341/jssms12018
论文

不同优化方法的混沌- RBF神经网络模型对大白菜短期价格预测的结果比较

    崔利国,李哲敏
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COMPARING WITH THE RESULTS OF SHORT-TERM PRICE FORECASTING FOR CABBAGE BY DIFFERENT OPTIMIZATION ALGORITHM OF CHAOS-RBF NEURAL NETWORK MODEL

    CUI Liguo, LI Zhemin
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摘要

以2008年1月21日至2012年5月13日的大白菜日度零售价格为研究对象,结合混沌理论和神经网络技术在处理非线性问题上的优势,尝试构建了一般混沌-RBF神经网络模型、 基于遗传算法优化的混沌-RBF神经网络模型和基于粒子群算法优化的混沌- RBF神经网络模型,并比较其不同的优化算法对于大白菜价格短期预测精度是否有提高.研究结论显示:基于粒子群算法优化的混沌-RBF神经网络模型在拟合效果和预测精度上均明显好于 其他两种混沌-RBF模型.这也显示了混沌神经网络技术在大白菜价格短期预测领域中具有广泛的应用前景.

Abstract

Based on the daily retail price of cabbages from January 21, 2008 to May 13, 2012, this paper tries to establish a general chaos-RBF neural network model, a chaos-RBF neural network model based on Genetic Algorithm optimization, and a chaos-RBF Neural network model based on Particle Swarm Optimization. This paper designs these three models by combining the advantages of chaos theory and neural network technology in the processing of the nonlinear problems. We compare the prediction accuracy of different optimization algorithms in the short-term price prediction of cabbages. The conclusion is that the chaos-RBF Neural network model based on Particle Swarm Optimization is significantly better than the other two kinds of chaos-RBF model in terms of the model fitting effect and the prediction accuracy. The result also shows that the chaotic neural network technology has a broad prospect of application in the field of short-term price prediction of cabbages.

关键词

混沌- RBF神经网络 / 短期价格预测 / 大白菜.

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崔利国,李哲敏. 不同优化方法的混沌- RBF神经网络模型对大白菜短期价格预测的结果比较. 系统科学与数学, 2013, 33(1): 45-54. https://doi.org/10.12341/jssms12018
CUI Liguo, LI Zhemin. COMPARING WITH THE RESULTS OF SHORT-TERM PRICE FORECASTING FOR CABBAGE BY DIFFERENT OPTIMIZATION ALGORITHM OF CHAOS-RBF NEURAL NETWORK MODEL. Journal of Systems Science and Mathematical Sciences, 2013, 33(1): 45-54 https://doi.org/10.12341/jssms12018
中图分类号: 92D50   
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