A dynamic SVR–ARMA model with improved fruit fly algorithm for the nonlinear fiber stretching process



The fiber stretching process plays the key role in the process of fiber production and its effects is measured by the stretching ratio. The stretching ratio is determined by the relative speed of the winding roller. The stretching ratio has impact on the performance of the final fiber filament and production directly. Focused on the importance of the stretching ratio, the support vector regression (SVR) predictive model, called nonlinear auto-regressive exogenous model, for the fiber stretching rate based on existing industry data is proposed. Furthermore, the fruit fly optimization algorithm inspired by immune mechanism and cooperation functional (IFOA) is presented, and then is used to optimize the parameters in SVR. Furthermore, taking into account the high cost and accurate precision of the fiber stretching process, a time series autoregressive moving average (ARMA) model is introduced to reduce the prediction error of the IFOA–SVR model. Simulations results demonstrate that the proposed IFOA–SVR method can increase the prediction accuracy than the traditional FOA and the SVR method, and the ARMA model is essential to modify the prediction error of the IFOA–SVR model.


SVR model ARMA model Fruit fly optimization algorithm Fiber stretching process Prediction 



This work was supported in part by the Key Project of the National Nature Science Foundation of China (No. 61134009), the National Nature Science Foundation of China (Nos. 61473077, 61473078, 61503075), Cooperative research funds of the National Natural Science Funds Overseas and Hong Kong and Macao scholars (No. 61428302), Program for Changjiang Scholars from the Ministry of Education, and International Collaborative Project of the Shanghai Committee of Science and Technology (No. 16510711100).


  1. Akaike H (1974) A new look at the statistical model identification. IEEE Trans Autom Control 19:716–723MathSciNetCrossRefMATHGoogle Scholar
  2. Aumi S, Prashant Mhaskar (2012) Integrating data-based modeling and nonlinear control tools for batch process control. AIChE J 58(7):2105–2119CrossRefGoogle Scholar
  3. Bazbouz MB, Stylios GK (2008) Novel mechanism for spinning continuous twisted composite nanofiber yams. Eur Polym J 44(1):1–12CrossRefGoogle Scholar
  4. Cao LJ, Tay FEH (2003) Support vector machine with adaptive parameters in financial time series forecasting. IEEE Trans Neural Netw 14(6):1506–1518CrossRefGoogle Scholar
  5. Carroll JR, Givens MP, Piefer R (1994) Design elements of the modem spinning control system. In: IEEE annual. Textile fiber film industrial technology conference, pp 4–5Google Scholar
  6. Chen JY, Yu J (2014) Independent component analysis mixture model based dissimilarity method for performance monitoring of non-gaussian dynamic processes with shifting operating conditions. Ind Eng Chem Res 53:5055–5066CrossRefGoogle Scholar
  7. Chen PW, Lin WY, Huang TH, Pan WT (2013) Using fruit fly optimization algorithm optimized grey model neural network to perform satisfaction analysis for e-business service. Appl Math Inf Sci 7:459–465CrossRefGoogle Scholar
  8. Gilan S, Jovein HB, Ali AR (2012) Hybrid support vector regression-particle swarm optimization for prediction of compressive strength and RCPT of concretes containing metakaolin. Constr Build Mater 34:321–329CrossRefGoogle Scholar
  9. Ismail F, Rahman MA, Mustafa A, Masuura T (2008) The effect of processing conditions on a polyacrylonitrile fiber produced by a solvent-free coagulation process. Mater Sci Eng 485(1–2):251–257CrossRefGoogle Scholar
  10. Jeffrey KCF (2008) An entire strategy for control of a calendar roller system. Part III: Intelligent settling time-optimal control. Text Res J 78(1):81–87MathSciNetCrossRefGoogle Scholar
  11. Kadlec P, Grbic R, Gabrys B (2011) Review of adaptation mechanism for data-driven soft sensors. Comput Chem Eng 5:1–24CrossRefGoogle Scholar
  12. Kang Q, Zhou M, An J, Wu Q (2013) Swarm intelligence approaches to optimal power flow problem with distributed generator failures in power networks. IEEE Trans Autom Sci Eng 10(2):343–353CrossRefGoogle Scholar
  13. Keerthi SS, Lin CJ (2003) Asymptotic behaviors of support vector machines with Gaussian kernel. Neural Comput 15:1667–1689CrossRefMATHGoogle Scholar
  14. Li H, Guo S, Li C, Sun J (2013) A hybrid annual power load forecasting model based on generalized regression neural network with fruit fly optimization algorithm. Knowl Based Syst 37(1):378–387CrossRefGoogle Scholar
  15. Liang X, Ding YS, Ren LH, Hao KR (2012) A bioinspired multilayered intelligent cooperative controller for stretching process of fiber production. IEEE Trans Syst Man Cybern Part C Appl Rev 42(3):367–377CrossRefGoogle Scholar
  16. Liang X, Ding YS, Ren LH, Hao KR, Jin YL (2014a) Data-driven cooperative intelligent controller based on the endocrine regulation mechanism. IEEE Trans Control Syst Technol 22(1):94–101CrossRefGoogle Scholar
  17. Liang X, Ding YS, Wang ZD, Hao KR, Hone K, Wang HP (2014b) Bidirectional optimization of the melting spinning process. IEEE Trans Cybern 44(2):240–251CrossRefGoogle Scholar
  18. Lin SM (2013) Analysis of service satisfaction in web auction logistics service using a combination of fruit fly optimization algorithm and general regression neural network. Neural Comput Appl 22(3):783–791CrossRefGoogle Scholar
  19. Lin B, Recke B, Renaudat P, Knudsen J, Jorgensen SB (2007) A systematic approach for soft sensor development. Comput Chem Eng 31(5–6):419–425CrossRefGoogle Scholar
  20. Pan WT (2012) A new fruit fly optimization algorithm: taking the financial distress model as an example. Knowl Based Syst 26:69–74CrossRefGoogle Scholar
  21. Qiao JH, Chai TY (2012) Soft measurement model and its application in raw meal calcination process. J Process Control 22(1):344–351CrossRefGoogle Scholar
  22. Shardt YAW, Huang B (2012) Tuning a soft sensor’s bias update term. 1. The open-loop case. Ind Eng Chem Res 51:4958–4967CrossRefGoogle Scholar
  23. Sudheer Ch, Anand N, Panigrahi BK, Mathur S (2013) Streamflow forecasting by SVM with quantum behaved particle swarm optimization. Neurocomputing 101:18–23CrossRefGoogle Scholar
  24. Vapnik V (1995) The nature of statistical learning theory. Springer, New YorkCrossRefMATHGoogle Scholar
  25. Wang David, Liu J, Rajagopalan S (2010) Data-driven soft sensor approach for quality prediction in a refining process. IEEE Trans Industr Inf 6(1):11–17CrossRefGoogle Scholar
  26. Wang JJ, Li L, Niu D, Tan ZF (2012) An annual load forecasting model based on support vector regression with differential evolution algorithm. Appl Energy 94:65–70CrossRefGoogle Scholar
  27. Xie L, Zhao Y, Aziz D, Jin X, Geng LT, Goberdhansingh E, Qi F, Huang B (2013) Soft sensor for online steam quality measurements of OTSGs. J Process Control 23(7):990–1000CrossRefGoogle Scholar
  28. Xu N, Ding YS, Hao KR (2014) Immunological mechanism inspired iterative learning control. Neurocomputing 145(5):392–401CrossRefGoogle Scholar
  29. Yu J (2013) A support vector clustering-based probabilistic method for unsupervised fault detection and classification of complex chemical processes using unlabeled data. AIChE J 59:407–419CrossRefGoogle Scholar
  30. Zhang GP (2003) Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50:159–175CrossRefMATHGoogle Scholar
  31. Zhang ZY, Wang T, Liu XG (2014) Melt index prediction by aggregated RBF neural networks trained with chaotic theory. Neurocomputing 131:368–376CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  1. 1.Engineering Research Center of Digitized Textile and Apparel TechnologyMinistry of EducationShanghaiPeople’s Republic of China
  2. 2.College of Information Science and TechnologyDonghua UniversityShanghaiPeople’s Republic of China
  3. 3.Department of Computer ScienceUniversity of SurreyGuildfordUK

Personalised recommendations