A REGRESSION MODEL FOR RE-VANADIUM USING PARALLEL KERNEL RBF NEURAL NETWORKS

Radial Basis Function Neural Networks (RBF NN) are frequently used for learning the rule of complex phenomenon and system. But kernel matrix computation for high dimensional data source demands heavy computing power. To shorten the computing time, the paper designs a parallel algorithm to compute the kernel function matrix of RBF NN and applies it to the prediction of converter re-vanadium modeling. This paper studies the possibility of using parallel kernel RBF regression for modeling an intelligent decision system for re-vanadium in metallurgical process. The paper then implements the algorithm on a cluster of computing workstations using MPI. Finally, we experiment with the practical data to study the speedups and accuracy of the algorithm. The proposed algorithm proves to be effective and practicable in its application.