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

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.

[1]  Fei Qi,et al.  Soft sensors for online steam quality measurements of OTSGs , 2013 .

[2]  Sen Guo,et al.  A hybrid annual power load forecasting model based on generalized regression neural network with fruit fly optimization algorithm , 2013, Knowl. Based Syst..

[3]  J. R. Carroll,et al.  Design elements of the modern spinning control system , 1994, Proceedings of 1994 IEEE/IAS Annual Textile, Fiber and Film Industry Technical Conference.

[4]  Jianjun Wang,et al.  An annual load forecasting model based on support vector regression with differential evolution algorithm , 2012 .

[5]  Su-Mei Lin,et al.  Analysis of service satisfaction in web auction logistics service using a combination of Fruit fly optimization algorithm and general regression neural network , 2011, Neural Computing and Applications.

[6]  Wei-Yuan Lin,et al.  Using Fruit Fly Optimization Algorithm Optimized Grey Model Neural Network to Perform Satisfaction Analysis for E-Business Service , 2013 .

[7]  Biao Huang,et al.  Tuning a Soft Sensor’s Bias Update Term. 1. The Open-Loop Case , 2012 .

[8]  Yongsheng Ding,et al.  Bidirectional Optimization of the Melting Spinning Process , 2014, IEEE Transactions on Cybernetics.

[9]  Bogdan Gabrys,et al.  Review of adaptation mechanisms for data-driven soft sensors , 2011, Comput. Chem. Eng..

[10]  Prashant Mhaskar,et al.  Integrating data-based modeling and nonlinear control tools for batch process control , 2011, Proceedings of the 2011 American Control Conference.

[11]  Yongsheng Ding,et al.  Data-Driven Cooperative Intelligent Controller Based on the Endocrine Regulation Mechanism , 2014, IEEE Transactions on Control Systems Technology.

[12]  Sten Bay Jørgensen,et al.  A systematic approach for soft sensor development , 2007, Comput. Chem. Eng..

[13]  Chung-Feng Jeffrey Kuo,et al.  An Entire Strategy for Control of a Calender Roller System. Part III: Intelligent Settling Time-optimal Control , 2008 .

[14]  Yongsheng Ding,et al.  A Bioinspired Multilayered Intelligent Cooperative Controller for Stretching Process of Fiber Production , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[15]  Chih-Jen Lin,et al.  Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel , 2003, Neural Computation.

[16]  Jie Yu,et al.  Independent Component Analysis Mixture Model Based Dissimilarity Method for Performance Monitoring of Non-Gaussian Dynamic Processes with Shifting Operating Conditions , 2014 .

[17]  Yongsheng Ding,et al.  Immunological mechanism inspired iterative learning control , 2014, Neurocomputing.

[18]  Ali Akbar Ramezanianpour,et al.  Hybrid support vector regression – Particle swarm optimization for prediction of compressive strength and RCPT of concretes containing metakaolin , 2012 .

[19]  Ahmad Fauzi Ismail,et al.  The effect of processing conditions on a polyacrylonitrile fiber produced using a solvent-free free coagulation process , 2008 .

[20]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[21]  Jie Yu,et al.  A support vector clustering‐based probabilistic method for unsupervised fault detection and classification of complex chemical processes using unlabeled data , 2013 .

[22]  Guoqiang Peter Zhang,et al.  Time series forecasting using a hybrid ARIMA and neural network model , 2003, Neurocomputing.

[23]  MengChu Zhou,et al.  Swarm Intelligence Approaches to Optimal Power Flow Problem With Distributed Generator Failures in Power Networks , 2013, IEEE Transactions on Automation Science and Engineering.

[24]  Tianyou Chai,et al.  Soft measurement model and its application in raw meal calcination process , 2012 .

[25]  Bijaya K. Panigrahi,et al.  Streamflow forecasting by SVM with quantum behaved particle swarm optimization , 2013, Neurocomputing.

[26]  Francis Eng Hock Tay,et al.  Support vector machine with adaptive parameters in financial time series forecasting , 2003, IEEE Trans. Neural Networks.

[27]  George K Stylios,et al.  Novel mechanism for spinning continuous twisted composite nanofiber yarns , 2008 .

[28]  Wen-Tsao Pan,et al.  A new Fruit Fly Optimization Algorithm: Taking the financial distress model as an example , 2012, Knowl. Based Syst..

[29]  A. D. Solomon,et al.  On surface effects in heat transfer calculations , 1981 .

[30]  H. Akaike.  A new look at the statistical model identification , 1974 .

[31]  Ting Wang,et al.  Melt index prediction by aggregated RBF neural networks trained with chaotic theory , 2014, Neurocomputing.