Data Pre-processing for a Neural Network Trained by an Improved Particle Swarm Optimization Algorithm
Tuan Linh, Thang Cao, Yukinobu Hoshino · International Journal of Computer Applications · 2016
This paper proposes an improved version of particle swarm optimization (PSO) algorithm for the training of a neural network (NN).An architecture for the NN trained by PSO (standard PSO, improved PSO) is also introduced.This architecture has a data preprocessing mechanism which consists of a normalization module and a data-shuffling module.Experimental results showed that the NN trained by improved PSO (IPSO) achieved better performance than both the NN trained by standard PSO and the NN trained by back-propagation (BP) algorithm.The effectiveness concerning the recognition rate and the minimum learning error of the data preprocessing modules (normalization module, data-shuffling module) was also demonstrated through the experiments.