关键词: 人工智能, 动态过程神经网络, 粒子群算法, 模式分类

Abstract: To overcome the shortcomings of slow convergent speed and low accuracy of the feedforward process neural network, an Improved Particle Swarm Optimization (IPSO) is proposed to train the dynamic process neural network. A fully connected feedforward dynamic process neural network can be converted into partially connected network by tuning the structure and choosing the connection weights of PNN simultaneously. This will result in significant cost reduction in implementation of the neural network. The dynamic process neural network trained by IPSO has been applied to Iris pattern classification. Results show that the modified particle swarm dynamic process neural network can accelerate the convergent speed and improve the accuracy.

Key words: artificial intelligence, improved dynamic process neural network(IDPNN), particle swarm optimization, pattern classification