Neural network training based on object-oriented adaptive particle swarm optimization
基于面向对象自适应粒子群算法的神经网络训练*

In view of the traditional neural network training algorithm defects of slow convergence speed and the low generalization, this paper proposed a novel object-oriented adaptive particle swarm optimization(OAPSO) algorithm in the neural network training. This algorithm enhanced the training speed and the generalization of network through improving the encoding method and the self-adapted search strategy of PSO. Then, used two standard data sets, Iris and Ionosphere, in the test. The experiments show that the neural network based on OAPSO algorithm is obviously superior to BP algorithm and standard PSO algorithm in the classification accuracy rate, and enhances the generalization and the optimized effect of the network. This algorithm has the performance of rapid global convergence.