Kernel-Based Random Vector Functional-Link Network for Fast Learning of Spatiotemporal Dynamic Processes
针对工业热过程中温度分布难以实时估计的问题，提出一种基于核的随机向量函数链接网络，利用Karhunen-Loève方法提取空间基函数，实现高学习速度下的时空建模，仿真验证其优于神经网络和最小二乘支持向量机。
Distributed parameter systems widely exist in many industrial thermal processes. Estimation of their temperature distribution in the entire operating area is not easy as the dynamics are time/space coupled, and there are only a few sensors available for measurement. In this paper, an effective spatiotemporal model is proposed for prediction of the temperature distribution. After the dominant spatial basis functions are obtained by the Karhunen-Loève method under the time/space separation, a kernel-based random vector functional-link network is developed for learning unknown temporal dynamics. After time/space synthesis, the spatiotemporal model can be constructed to effectively estimate the temperature distribution in high learning speed. The generalization performance of this model is discussed using Rademacher complexity. Simulations on two typical industrial thermal processes show that the proposed method has superior model performance than neural networks and least square support vector machine.