A Neural Network Pruning Method Optimized with PSO Algorithm

A neural network (NN) pruning method optimized with particle swarm optimization (PSO) algorithm is proposed in this paper. Correlation merging algorithm is an important pruning method in NN structure design. Unlike general training method with back-propagation (BP), this paper uses PSO algorithm in the pruning process. The PSO is used to optimize the initial parameters of the NN, including the weights and biases etc. The experiment results show that the method in the paper above conventional one has greater improvement in both accuracy and velocity of convergence for NN.