Improved particle swarm optimized back propagation neural network and its application to production quality modeling

In order to solve the difficulties of tendency to local optima in conditional optimization algorithms for back propagation neural network (BPNN), with improvements in the strategy for updating the particle's velocity and location, this paper proposed a new back propagation neural network modeling method based on improved particle swarm optimization. The data from sinc function, Boston housing problem and the real strip hot-dip galvanizing production in an iron and steel corporation were used for verification. The results show that, compared with the standard BPNN and support vector machine algorithms, the proposed method can effectively help the BPNN to get a better regression precision and prediction performance.