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The knowledge about training feed forward neural networks (FNNs) is an important and complex issue in the supervised learning field. In the process of learning, the FNNs system involves some input parameters such as connection weights and biases, which may greatly influence the performance of FNNs training. In this paper, a newly developed meta-heuristic method, named social learning particle swarm optimization (SLPSO), is trying to find the optimal combination of connection weights and biasesdoi:10.14704/nq.2018.16.6.1588 fatcat:dgyarwia5jh7lh3v7oladzpomy