Research and application of feature extraction derived functional link neural network

doi:10.11949/j.issn.0438-1157.20171416

Abstract

Abstract:

Traditional functional link neural network (FLNN) cannot effectively model multi-dimensional, noisy and strongly coupled data in chemical process. A principal component analysis based FLNN (PCA-FLNN) model was proposed to improve modeling effectiveness. Feature extraction of FLNN function extension block not only removed linear correlations between variables but also selected main components of data, which complexity of FLNN learning data was alleviated. The proposed PCA-FLNN model was used to simulate an UCI Airfoil Self-Noise data and purified terephthalic acid (PTA) production process. Simulation results indicated that PCA-FLNN can achieve faster convergence speed with higher modeling accuracy than traditional FLNN.

Key words: functional link artificial neural network, feature extraction, process modeling, purified terephthalic acid

摘要：

对于化工过程中带噪声、强耦合的高维数据建模问题，常规的函数连接神经网络（functional link neural networks，FLNN）无法有效地进行处理。为解决该问题，提出一种基于主元分析（principal components analysis，PCA）的函数连接神经网络（PCA-FLNN）。通过对FLNN的函数扩展层进行特征提取，不仅去除变量间的线性相关关系，而且提取数据的主成分，进而简化FLNN学习数据的复杂度。为验证所提方法的有效性，首先采用UCI数据Airfoil Self-Noise对其性能进行验证；随后将所提的方法应用于精对苯二甲酸（purified terephthalic acid，PTA）生产过程建模；与传统FLNN进行对比，标准数据和工业数据的仿真结果表明，PCA-FLNN在处理复杂化工过程数据时具有收敛速度快和建模精度高的特点。

关键词: 函数连接神经网络, 特征提取, 过程建模, 精对苯二甲酸

CLC Number:

TP29

ZHU Qunxiong, ZHANG Xiaohan, GU Xiangbai, XU Yuan, HE Yanlin. Research and application of feature extraction derived functional link neural network[J]. CIESC Journal, 2018, 69(3): 907-912.

朱群雄, 张晓晗, 顾祥柏, 徐圆, 贺彦林. 基于特征提取的函数连接神经网络研究及其化工过程建模应用[J]. 化工学报, 2018, 69(3): 907-912.

References 30

[1]	DING S F, SU C Y, YU J Z. An optimizing BP neural network algorithm based on genetic algorithm[J]. Artificial Intelligence Review, 2011, 36(2):153-162.
[2]	MOALLEM P, MONADJEMI S A. An efficient MLP-learning algorithm using parallel tangent gradient and improved adaptive learning rates[J]. Connection Science, 2010, 22(4):373-392.
[3]	刘菲菲, 彭荻, 贺彦林, 等. 基于极限学习机的过程神经网络研究及化工应用[J]. 上海交通大学学报, 2014, 48(7):977-981. LIU F F, PENG D, he Y L, et al. Research and chemical application of extreme learning based process neural network[J]. Journal of Shanghai Jiao Tong University, 2014, 48(7):977-981.
[4]	HE Y L, ZHU Q X. A novel robust regression model based on functional link least square (FLLS) and its application to modeling complex chemical processes[J]. Chemical Engineering Science, 2016, 153:117-128.
[5]	耿志强, 武开英, 韩永明. 基于层次分析的FLNN神经网络研究及应用[J]. 化工学报, 2016, 67(3):805-811. GENG Z Q, WU K Y, HAN Y M. Research and application of FLNN neural network based on AHP[J]. CIESC Journal, 2016, 67(3):805-811.
[6]	MISHRA S K, PANDA G, MEHER S, et al. Exponential functional link artificial neural networks for denoising of image corrupted by Gaussian noise[C]//Proceedings of the 2009 International Conference on Advanced Computer Control. Washington DC, USA:IEEE Computer Society, 2009:355-359.
[7]	MAJHI R, PANDA G, SAHOO G. Development and performance evaluation of FLANN based model for forecasting of stock markets[J]. Expert Systems with Applications:an International Journal, 2009, 36(3):6800-6808.
[8]	HUANG S L, HAO K S, ZHAO W. New improved FLANN approach for dynamic modelling of sensors[J]. International Journal of Computer Applications in Technology, 2011, 41(1/2):4-10.
[9]	ZHU B, CHEN Z S, HE Y L, et al. A novel nonlinear functional expansion based PLS (FEPLS) and its soft sensor application[J]. Chemometrics & Intelligent Laboratory Systems, 2017, 161:108-117.
[10]	何晓群. 多元统计分析[M]. 北京:中国人民大学出版社, 2012:135. HE X Q. Multivariate Statistical Analysis[M]. Beijing:China Renmin University Press, 2012:135.
[11]	LEE J M, YOO C K, CHOI S W, et al. Nonlinear process monitoring using kernal principal component analysis[J]. Chem. Eng. Sci., 2004, 59(1):223-234
[12]	CHEN Y. Reference-related component analysis:a new method inheriting the advantages of PLS and PCA for separating interesting information and reducing data dimension[J]. Chemometrics and Intelligent Laboratory Systems, 2016, 156:196-202.
[13]	LU N, YAO Y, GAO F, et al. Two-dimensional dynamic PCA for batch process monitoring[J]. AIChE J., 2005, 51(12):3300-3304.
[14]	SONG S O, SHIN D, YOON E S. Analysis of abnormality detection properties of nonlinear PCA methods[J]. IFAC Proceedings Volumes, 2001, 34(27):309-314.
[15]	MOORE B. Principal component analysis in linear systems:controllability, observability, and model reduction[J]. IEEE Transactions on Automatic Control, 1981, 26(1):17-32.
[16]	JACKSON J E, MUDHOLKAR G S. Control procedures for residuals associated with principal component analysis[J]. Technometrics, 1979, 21 (3):341-349.
[17]	YUAN Y B, WANGY Y G, CAO F L. Optimization approximation solution for regression problem based on extreme learning machine[J]. Neurocomputing, 2011, 74(16):2475-2482.
[18]	LI C F, DAI Y Y, ZHAO J J, et al. Remote sensing monitoring of volcanic ash clouds based on PCA method[J]. Acta Geophysica, 2015:432-450.
[19]	PENG D Z, ZHANG Y. Dynamics of generalized PCA and MCA learning algorithms[J]. IEEE Transactions on Networks, 2007, 18(6):1777-1784.
[20]	ZHANG L, DONG W, ZHANG D, et al. Two-stage image denoising by principal component analysis with local pixel grouping[J]. Pattern Recognition, 2010, 43(4):1531-1549.
[21]	LIANG N Y, HUANG G B. A fast and accurate online sequential learning algorithm for feed-forward networks[J]. IEEE Transactions on Networks, 2006, 17(6):1411-1423.
[22]	朱志洁, 张宏伟, 韩军, 等. 基于PCA-BP神经网络的煤与瓦斯突出预测研究[J]. 中国安全科学报, 2013, 23(4):45-50. ZHU Z J, ZHANG H W, HAN J, et al. Prediction of coal and gas outburst based on PCA-BP neural network[J]. China Safety Science Journal, 2013, 23(4):45-50.
[23]	贺彦林, 王晓, 朱群雄. 基于主成分分析-改进的极限学习机方法的精对苯二甲酸醋酸含量软测量[J]. 控制理论与应用, 2015, 32(1):80-85. HE Y L, WANG X, ZHU Q X. Modeling of acetic acid content in purified terephthalic acid solvent column using principal component analysis based improved extreme learning machine[J]. Control Theory & Applications, 2015, 32(1):80-85.
[24]	SHARIFI R, LANGARI R. Nonlinear sensor fault diagnosis using mixture of probabilistic PCA models[J]. Mechanical Systems & Signal Processing, 2017, 85:638-650.
[25]	Pao Y H, Philips S M, Sobajic D J. Neural-net computing and intelligent control systems[J]. International Journal of Control, 1992, 56(2):263-289.
[26]	吕久旭, 吴乐南. 基于函数连接型神经网络的非线性滤波[J]. 长春工业大学学报, 2006, 27(4):305-307. LÜ J X, WU L N. Nonlinear filtering based on function connected neural network[J]. Journal of Changchun University of Technology, 2006, 27(4):305-307.
[27]	叶世伟, 史忠植. 神经网络原理[M]. 北京:机械工业出版社, 2004:10-12. Ye S W, Shi Z Z. Neural Networks:a Comprehensive Foundation[M]. Beijing:China Machine Press, 2004:10-12.
[28]	HE Y L, XU Y, GENG Z Q, et al. Hybrid robust model integrating with partial least square (IFLNN-PLS) and its application to predicting key process variables[J]. ISA Transactions, 2016, 61:155-166.
[29]	MOHAMMAD S O. Optimizing functional link neural network learning using modified bee colony on multi-class classifications[M]//JEONG H S, OBAIDAT M, YEN N, PARK J, ed. Advances in Computer Science and Its Applications. Lecture Notes in Electrical Engineering. Berlin:Springer, 2014:153-159.
[30]	GHAZALI R, BAKAR Z A, HASSIM Y M M, et al. Functional link neural network with modified cuckoo search training algorithm for physical time series forecasting[M]//HUANG D S, BEVILACQUA V, PREMARATNE P, ed. Intelligent Computing Theory. Berlin:Springer, 2014:285-291.