Abstract
A new algorithm for single hidden layer feedforward neural networks (SLFN), Orthogonal Basis Extreme Learning (OBEL) algorithm, is proposed and the algorithm derivation is given in the paper. The algorithm can decide both the NNs parameters and the neuron number of hidden layer(s) during training while providing extreme fast learning speed. It will provide a practical way to develop NNs. The simulation results of function approximation showed that the algorithm is effective and feasible with good accuracy and adaptability.
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© 2015 Springer International Publishing Switzerland
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Li, Y., Li, Y., Wan, X. (2015). Orthogonal Basis Extreme Learning Algorithm and Function Approximation. In: Hu, X., Xia, Y., Zhang, Y., Zhao, D. (eds) Advances in Neural Networks – ISNN 2015. ISNN 2015. Lecture Notes in Computer Science(), vol 9377. Springer, Cham. https://doi.org/10.1007/978-3-319-25393-0_39
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DOI: https://doi.org/10.1007/978-3-319-25393-0_39
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Publisher Name: Springer, Cham
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Online ISBN: 978-3-319-25393-0
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