查看文章

Artificial neural network (ANN) has a wide variety of practice for the solution of problems in the area of data classification. Back propagation algorithm is a famous neural network (NN) traditional training approach. Hence, this classical training technique has many drawbacks like stuck in the local minima and maximum number of iterations required. Particle Swam Optimization (PSO) has been widely applied for the solutions of data classification problems. Population initialization is a vital factor in PSO algorithm, which considerably influences the diversity and convergence during the PSO's process. In this paper, the training of the ANN has been implemented with new initialization technique by using low discrepancies sequence, Torus termed as TO-PSO. In this paper, a detailed comparative performance analysis for the training of neural network is observed on nine benchmark data sets taken from UCI repository …