A new way of data fitting and parameter optimization for complex chemical reaction kinetic model was developed based on the integration of numerical analysis and sequential quadratic programming methods, by which not only the weakness of traditional algorithm failing of direct constraint analysis could be overcome, but also the quicker convergence rate and less fitting error could be obtained.

In this paper, when evolutionary programming and sequential quadratic programming are applied to the structure optimization of feed-forward neural networks, a learning algorithm is proposed.