Optimization research on Artificial Neural network Model

Optimization Research on Artificial Neural Tree Network Model is divided into two parts: optimizing topology structure and optimizing parameters. For optimizing topology structure, building-block-library based genetic programming algorithm, anarchical variable probability vector based probabilistic incremental program evolution algorithm and tree-encoded based particle swarm optimization algorithm are proposed. The above algorithms can effectively reduce the number of invalid individuals generated in evolution process, improve the convergence speed and error precision of the NTNM. For optimizing parameters, differential evolution algorithm is introduced. It has characteristics of less parameters to control, easier to implement and uneasy to fall into local minimum, etc. which make it very suitable for the optimization of parameters.