An intelligent model of LWA using distributed kernel

This paper researches the possibility of using locally weighted algorithm for intelligent modeling of a nonlinear system for vanadium extraction in metallurgical process and proposes some optimized methods by finding the optimized regression coefficients by gradient descent and kernel function bandwidth by weighted distance. But kernel matrix computation for high dimensional data source demands heavy computing power. To overcome the computational difficulties of kernel functions and shorten the computing time, the paper designs a distributed algorithm to compute the kernel function matrix of LWA. The paper then implements the algorithm on a cluster of computing workstations using MPI. This paper studies the possibility of LWA using distributed kernel computing for predictive modeling for vanadium extraction in metallurgical process. Finally, the practical data are used to study the speedups and accuracy of the algorithm. The experimental results show that optimized locally weighted algorithm using distributed kernel outperforms the traditional RBF, RFWR and LWPR methods when significant amounts of noise are added, and the computing time has been shortened.