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.