Skip to main content
Log in

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

  • Published:
Natural Computing Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  • Akaike H (1974) A new look at the statistical model identification. IEEE Trans Autom Control 19:716–723

    Article  MathSciNet  Google Scholar 

  • Aumi S, Prashant Mhaskar (2012) Integrating data-based modeling and nonlinear control tools for batch process control. AIChE J 58(7):2105–2119

    Article  Google Scholar 

  • Bazbouz MB, Stylios GK (2008) Novel mechanism for spinning continuous twisted composite nanofiber yams. Eur Polym J 44(1):1–12

    Article  Google Scholar 

  • Cao LJ, Tay FEH (2003) Support vector machine with adaptive parameters in financial time series forecasting. IEEE Trans Neural Netw 14(6):1506–1518

    Article  Google Scholar 

  • 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 4–5

  • 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 53:5055–5066

    Article  Google Scholar 

  • 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 7:459–465

    Article  Google Scholar 

  • 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 34:321–329

    Article  Google Scholar 

  • 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):251–257

    Article  Google Scholar 

  • 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):81–87

    Article  Google Scholar 

  • Kadlec P, Grbic R, Gabrys B (2011) Review of adaptation mechanism for data-driven soft sensors. Comput Chem Eng 5:1–24

    Article  Google Scholar 

  • 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):343–353

    Article  Google Scholar 

  • Keerthi SS, Lin CJ (2003) Asymptotic behaviors of support vector machines with Gaussian kernel. Neural Comput 15:1667–1689

    Article  Google Scholar 

  • 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):378–387

    Article  Google Scholar 

  • 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):367–377

    Article  Google Scholar 

  • 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):94–101

    Article  Google Scholar 

  • 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):240–251

    Article  Google Scholar 

  • 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):783–791

    Article  Google Scholar 

  • Lin B, Recke B, Renaudat P, Knudsen J, Jorgensen SB (2007) A systematic approach for soft sensor development. Comput Chem Eng 31(5–6):419–425

    Article  Google Scholar 

  • Pan WT (2012) A new fruit fly optimization algorithm: taking the financial distress model as an example. Knowl Based Syst 26:69–74

    Article  Google Scholar 

  • Qiao JH, Chai TY (2012) Soft measurement model and its application in raw meal calcination process. J Process Control 22(1):344–351

    Article  Google Scholar 

  • Shardt YAW, Huang B (2012) Tuning a soft sensor’s bias update term. 1. The open-loop case. Ind Eng Chem Res 51:4958–4967

    Article  Google Scholar 

  • Sudheer Ch, Anand N, Panigrahi BK, Mathur S (2013) Streamflow forecasting by SVM with quantum behaved particle swarm optimization. Neurocomputing 101:18–23

    Article  Google Scholar 

  • Vapnik V (1995) The nature of statistical learning theory. Springer, New York

    Book  Google Scholar 

  • 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):11–17

    Article  Google Scholar 

  • 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 94:65–70

    Article  Google Scholar 

  • 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):990–1000

    Article  Google Scholar 

  • Xu N, Ding YS, Hao KR (2014) Immunological mechanism inspired iterative learning control. Neurocomputing 145(5):392–401

    Article  Google Scholar 

  • 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 59:407–419

    Article  Google Scholar 

  • Zhang GP (2003) Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50:159–175

    Article  Google Scholar 

  • Zhang ZY, Wang T, Liu XG (2014) Melt index prediction by aggregated RBF neural networks trained with chaotic theory. Neurocomputing 131:368–376

    Article  Google Scholar 

Download references

Acknowledgements

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).

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Lihong Ren or Yongsheng Ding.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Guo, F., Ren, L., Jin, Y. et al. A dynamic SVR–ARMA model with improved fruit fly algorithm for the nonlinear fiber stretching process. Nat Comput 18, 747–756 (2019). https://doi.org/10.1007/s11047-016-9601-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11047-016-9601-2

Keywords

Navigation