Modified particle swarm dynamic process neural network and its application

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.