Dual least squares support vector machines based spatiotemporal modeling for nonlinear distributed thermal processes
2017
Abstract In this paper, a dual least squares support vector machines (LS-SVM) is proposed to model the thermal process. The infinite-dimensional system is first transformed into a finite-dimensional system through space-time separation. Then, the dual LS-SVM model is to approximate the two nonlinearities embedded in the system. Through space-time synthesis, the dual LS-SVM based spatiotemporal model is able to approximate the complex DPS with inherent coupled nonlinearities. The generalization performance of the proposed model is discussed using Rademacher complexity. Finally, simulations on a curing process demonstrate the effectiveness of the proposed modeling method.
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