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椭球定界算法在混合建模中的应用研究

DOI: 10.3724/SP.J.1004.2014.01875, PP. 1875-1881

Keywords: 混合建模,软测量,椭球定界,神经网络

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Abstract:

?并行结构混合建模主要由机理模型与误差补偿模型组成.一般地,误差补偿模型不宜过于复杂,且模型应具有校正功能,以免精度随时间不断下降.针对这个问题,本文选择单层神经网络作为误差补偿模型,并将椭球定界算法应用于单层神经网络的参数更新,不仅能够保证建模误差稳定有界,同时能够提高网络的收敛速度.将提出的方法应用于氧化铝生产过程,改进了原有的苛性碱和氧化铝组分浓度软测量方法.实验研究结果表明,椭球定界算法的应用提高了模型的精度和网络的收敛速度.除此之外,在存在噪声干扰下,改进的方法比原有方法更稳定,进一步证明了方法的有效性和优越性.

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