基于AFSA-BPNN的MRVM模型非线性融合建模
发布时间：2018-08-07 18:19
【摘要】：针对化工过程强非线性和多工况的特性,提出了一种基于BP神经网络(BPNN)有效非线性融合多关联向量机(MRVM)的建模方法.首先选择不同的核函数,采用样本数据建立单一RVM子模型;然后利用BPNN的强非线性拟合能力,对各子模型的预测信息进行非线性融合,并采用人工鱼群算法(AFSA)对BPNN的初始权重和阈值进行优化;最终建立MRVM非线性融合模型.将该建模方法应用于甲醇制烯烃生产过程(MTO)乙烯收率预测研究中,研究结果表明:与单一RVM模型和最优加权组合模型相比,基于MRVM的非线性融合模型具有更佳的预测精度.
[Abstract]:A modeling method based on BP neural network (BPNN) effective nonlinear fusion of multi-correlation vector machine (MRVM) is proposed for the characteristics of strong nonlinearity and multi-working conditions in chemical process. Firstly, different kernel functions are selected, and a single RVM submodel is built by using sample data. Then, the prediction information of each sub-model is fused by using the strong nonlinear fitting ability of BPNN. The initial weight and threshold of BPNN are optimized by artificial fish swarm algorithm (AFSA), and the nonlinear fusion model of MRVM is established. The modeling method is applied to the prediction of (MTO) ethylene yield in methanol olefin production process. The results show that the nonlinear fusion model based on MRVM has better prediction accuracy than single RVM model and optimal weighted combination model.
【作者单位】： 浙江工业大学化学工程学院;浙江省生物燃料利用技术研究重点实验室;
【基金】：国家自然科学基金资助项目(21676251)
【分类号】：TP18;TQ018
，
本文编号：2170924
[Abstract]:A modeling method based on BP neural network (BPNN) effective nonlinear fusion of multi-correlation vector machine (MRVM) is proposed for the characteristics of strong nonlinearity and multi-working conditions in chemical process. Firstly, different kernel functions are selected, and a single RVM submodel is built by using sample data. Then, the prediction information of each sub-model is fused by using the strong nonlinear fitting ability of BPNN. The initial weight and threshold of BPNN are optimized by artificial fish swarm algorithm (AFSA), and the nonlinear fusion model of MRVM is established. The modeling method is applied to the prediction of (MTO) ethylene yield in methanol olefin production process. The results show that the nonlinear fusion model based on MRVM has better prediction accuracy than single RVM model and optimal weighted combination model.
【作者单位】： 浙江工业大学化学工程学院;浙江省生物燃料利用技术研究重点实验室;
【基金】：国家自然科学基金资助项目(21676251)
【分类号】：TP18;TQ018
，
本文编号：2170924
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