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一种部分输入自调整神经网络及其在非线性数据重构中的应用*

, PP. 800-804

Keywords: 主元分析(PCA),数据重构,部分输入自调整神经网络,非线性过程

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Abstract:

实际工业过程大部分是非线性过程,其遗失数据的重构问题不能采用现有的线性数据重构方法来解决.本文提出一种部分输入自调整神经网络,以待求的重构变量作为要调整的网络输入.与传统网络不同的是,该网络的权值和阈值先由另外的神经网络训练求得,通过神经网络后向传递算法只需对网络的部分输入值进行训练,这样将非线性数据重构问题转化为部分输入神经网络的训练问题.仿真结果验证本文方法的有效性.

References

[1]  Walczak B, Massart D L. Dealing with Missing Data: PartⅠ. Chemometrics and Intelligent Laboratory Systems, 2001, 58(1): 15-27
[2]  Walczak B, Massart D L. Dealing with Missing Data: Part Ⅱ. Chemometrics and Intelligent Laboratory Systems, 2001, 58(1): 29-42
[3]  Nelson P R C, Taylar P A, MacGregor J F. Missing Data Methods in PCA and PLS: Score Calculations with Incomplete Observation. Chemometrics and Intelligent Laboratory Systems, 2001, 35(1): 45-65
[4]  Nelson P R C, Taylar P A, MacGregor J F. The Impact of Missing Measurements on PCA and PLS Prediction and Monitoring Application. Chemometrics and Intelligent Laboratory Systems, 2006, 80(1): 1-12
[5]  Goulding P R, Lennox B, Sandoz D J, et al. Fault Detection in Continuous Processes Using Multivariate Statistical Methods. International Journal of Systems Science, 2000, 31(11): 1459-1471
[6]  Lieftucht D, Kruger U, Irwin G W. Improved Diagnosis of Sensor Faults Using Multivariate Statistics // Proc of the American Control Conference. Boston, USA, 2004, Ⅴ: 4403-4407
[7]  Lee J M, Yoo C, Choi S W, et al. Nonlinear Process Monitoring Using Kernel Principal Component Analysis. Chemical Engineering Science, 2004, 59(1): 223-234
[8]  Tan S, Mavrovouniotis M L. Reducing Data Dimensionality through Optimizing Neural Network Inputs. AIChE Journal, 1995, 41(6): 1471-1480
[9]  Kramer M A. Nonlinear Principal Component Analysis Using Autoassociative Neural Networks. AIChE Journal, 1991, 37(2): 233-243
[10]  Saegusa R, Sakana H, Hashimoto S. Nonlinear Principal Analysis to Preserve the Order of Principal Components. Neurocomputing, 2004, 61(1): 57-70
[11]  Zhao Zhonggai, Liu Fei, Xu Baoguo. Nonlinear Principal Component Analysis Based on Hierarchical Input-Training Neural Network. Information and Control, 2005, 34(6): 656-659 (in Chinese) (赵忠盖,刘 飞,徐保国.一种基于分级输入训练神经网络的非线性主元分析.信息与控制, 2005, 34(6): 656-659)
[12]  Dong D, McAvoy T J. Nonlinear Principal Component Analysis Based on Principal Curves and Neural Networks. Computers and Chemical Engineering, 1996, 20(1): 65-78

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