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荣德生,胡举爽,赵君君,杨学鹏.基于数据融合IGA-RGRNN低阶煤制甲烷产量预测模型[J].电源学报,2018,16(1):178-184
基于数据融合IGA-RGRNN低阶煤制甲烷产量预测模型
Prediction Model of Methane Yield from Low-rank Coal Based on Data Fusion and IGA-RGRNN Algorithm
投稿时间:2015-12-31  修订日期:2016-08-28
DOI:10.13234/j.issn.2095-2805.2018.1.178
中文关键词:  广义旋转回归神经网络  改进遗传算法  数据融合  甲烷产量  预测模型
英文关键词:rotated generalized regression neural network (RGRNN)  improved genetic algorithm  data fusion  meth-ane yield  prediction model
基金项目:国家自然科学基金资助项目(51177067)
作者单位E-mail
荣德生 辽宁工程技术大学电气与控制工程学院, 葫芦岛 125105  
胡举爽 辽宁工程技术大学电气与控制工程学院, 葫芦岛 125105 1403436227@qq.com 
赵君君 辽宁工程技术大学电气与控制工程学院, 葫芦岛 125105  
杨学鹏 辽宁工程技术大学电气与控制工程学院, 葫芦岛 125105  
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中文摘要:
      为了提高智能系统的准确性与快速性,针对多传感器网络,提出了一种以融合技术为数据基础与改进遗传算法-广义旋转回归神经网络IGA-RGRNN(improved genetic algorithm and rotated generalized regression neural ne-twork)算法相结合的预测模型。利用RGRNN强大的非线性随机变量的处理能力,把预测理论引入改进遗传算法循环中,将该模型应用于低阶煤制甲烷产量预测过程,并对预测模型效果进行实验验证。实验结果表明,基于数据融合IGA-RGRNN低阶煤制甲烷产量预测模型的相对误差最大值为2.99%,相对误差最小值为0.25%,相对误差平均值为1.76%,相较其他预测模型具有泛化能力更强和预测精度更高的优势,为低阶煤制甲烷产量预测提供一种新的途径。
英文摘要:
      To improve the accuracy and rapidity of intelligent systems, a prediction model based on data fusion and combined with improved genetic algorithm and rotated generalized regression neural network(IGA-RGRNN) was propos-ed for multi-sensor network. By taking advantage of RGRNN's strong processing capability to handle nonlinear random variables, prediction theory was introduced to the iterations of IGA. The proposed model was applied to the prediction process of methane yield from low-rank coal, and the prediction effect was verified by experiments. Experimental results show that the maximum, minimum, and average relative errors of the prediction model based on data fusion and IGA-RGRNN were 2.99%, 0.25%, and 1.76%, respectively. Compared with other prediction models, the proposed model has advantages of stronger generalization capability and higher prediction accuracy, thus it can provide a new approach for the prediction of methane yield from low-rank coal.
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