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Training Feedforward Neural Networks Using Social Learning Particle Swarm Optimization- A Case Comparison Study on Electrical System | Huang | NeuroQuantology


DOI: 10.14704/nq.2018.16.6.1588

Training Feedforward Neural Networks Using Social Learning Particle Swarm Optimization- A Case Comparison Study on Electrical System

Yuansheng Huang, Ying Qiao, Yuelin Gao

Abstract


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 biases for FNNs, which is often used to deal with power load forecasting problem. In the numerical experiments, a case on the power load forecasting problem is employed to verify the effectiveness of SLPSO. The experiment results indicate that SLPSO has the advantages on the training accuracy and testing accuracy with respect to other six state-of-the-art intelligent optimization algorithms.

Keywords


Training Feed Forward Neural Networks, Social Learning Particle Swarm Optimization, Particle Swarm Optimization, Electrical System

Full Text:

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References


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Supporting Agencies



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