A soft sensing method of billet surface temperature based on ILGSSA-LSSVM

Abstract

Abstract It is difficult to measure the surface temperature of continuous casting billet, which results in the lack of important feedback parameters for further scientific control of the billet quality. This paper proposes a sparrow search algorithm to optimize the Least Square Support Vector Machine (LSSVM) model for surface temperature prediction of the billet, which is further improved by Logistic Chaotic Mapping and Golden Sine Algorithm (Improve Logistic Golden Sine Sparrow Search Algorithm LSSVM, short name ILGSSA-LSSVM). Using the Improved Logistic Chaos Mapping and Golden Sine Algorithm to find the optimal initial sparrow population, the value of penalty factor $$\gamma$$ γ and kernel parameter $$\sigma$$ σ for LSSVM are calculated. Global optimization method is adopted to find the optimal parameter combination, so that the negative influence of randomly initializing parameters on the prediction accuracy would be reduced. Our proposed ILGSSA-LSSVM soft sensing model is compared respectively with traditional Least Square Support Vector Machine, BP neural network and Gray Wolf optimized Least Square Support Vector Machine, results show that proposed model outperformed the others. Experiments show that the maximum error of ILGSA-LSSVM soft sensing model is 3.85733 °C, minimum error is 0.0174 °C, average error is 0.05805 °C, and generally outperformed other comparison models.