A Novel Spatiotemporal LS-SVM Method for Complex Distributed Parameter Systems With Applications to Curing Thermal Process

Abstract

The least-squares support vector machine (LS-SVM) has been successfully used to model nonlinear time dynamics; however, it does not have the capability to handle space information and is, therefore, unable to model the complex nonlinear distributed parameter systems (DPS). Here, we propose a spatiotemporal LS-SVM modeling method for complex nonlinear DPS. The space kernel function is developed to describe the nonlinear correlation between space locations. The time Lagrange multiplier represents the time dynamics. The integration of the space kernel function and the time Lagrange multiplier can reconstruct the nonlinear spatiotemporal dynamics of the DPS. The spatiotemporal LS-SVM method accounts for the time dynamics and the space distribution nature of the DPS, enabling it to adapt well to real-time spatiotemporal variation. The successful application of this spatiotemporal LS-SVM method to a practical curing thermal process and its comparison with several common DPS modeling methods demonstrate its superiority in the modeling of the unknown nonlinear distributed parameter process.