Optimization of Flexible Neural Tree Based on Improved Particle Swarm
基于改进粒子群算法的柔性神经树优化①

The Neural Tree uses a tree structure coding. It has good predictive ability and function approximation capabilities. In the model, parameters are usually optimized with particle swarm optimization algorithm, but the traditional particle swarm algorithm has following shortcomings like being easily trapped in local optimal value, being slow and having low accuracy in convergence in the later period of the evolution. It affects the performance of neural tree. This paper applies a new improved particle swarm optimization algorithm to the neural tree model, and compares it with the traditional particle swarm algorithm in the application of flexible neural tree. It shows that the improved particle swarm algorithm has better convergence accuracy, thus to improve the performance of the flexible neural tree.