Integrative improved particle swarm optimization neural network arithmetic

The particle swarm optimization arithmetic is an excellent optimization arithmetic that can solve the non-linear, un-fluxionary and multi-peak value optimizing problems. But in the process of looking for the excellent result, it is easily appear the phenomenon of speed becoming slow and precocious. The learning arithmetic of back propagation is base on the essence of grads descending, so there are inevitably problems of it is easy to get into partial least extremum, slowly constringency speed, long training time and so on. Improve the arithmetic at intensifying multiformity of particles and escaping the precocity of swarm, and put forward a particle swarm optimization neural network arithmetic based on the improved arithmetic. Prove the validity of the improving by the simulant experiments on the IRIS database.