Fast Evolutionary Programming-based Hybrid Multi-Layer Feedforward Neural Network for predicting Grid-Connected Photovoltaic system output

Abstract

This paper presents a Hybrid Multi-Layer Feedforward Neural Network (HMLFNN) technique for predicting the output from a Grid-Connected Photovoltaic (GCPV) system. In the proposed HMLFNN, Fast Evolutionary Programming (FEP) was employed to optimize the training process of the Multi-Layer Feedforward Neural Network (MLFNN). FEP was used to select the optimal… (More)

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Cite this paper

@article{Sulaiman2012FastEP, title={Fast Evolutionary Programming-based Hybrid Multi-Layer Feedforward Neural Network for predicting Grid-Connected Photovoltaic system output}, author={S. I. Sulaiman and T. K. Abdul Rahman and Ismail Musirin and Sulaiman Shaari}, journal={2012 IEEE 8th International Colloquium on Signal Processing and its Applications}, year={2012}, pages={44-47} }