Abstract
Extreme learning machine is a new feedforward neural network. Compared with the traditional neural network, it has the advantages of simple network construction and fast learning. However, as the least square method is used to solve the optimal output weight, the extreme learning machine has some problems such as weak anti-interference ability, poor stability and over-fitting. For the above problems, L2-norm regularization is adopted to optimize the extreme learning machine model, and minimize the training error and output weight by determining the regular parameters in this paper. Simulation results on standard data sets shows that optimization model by L2-norm regularization can significantly improve the stability and anti-interference of model.
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Acknowledgements
This study was funded by Anhui Provincial Natural Science Foundation (1808085MF202), Anhui Provincial Major Projects of Scientific Research of Universities (KJ2018ZD036), Fuyang Normal University PhD Research Project (2018kyqd0028), Horizontal Cooperation Project between Fuyang government and Fuyang Normal Teachers College (XDHX201706), Anhui Provincial Massive Open Online Course (MOOC) Demonstration Project (2019mooc203), Anhui Provincial Key Teaching research projects (2017jyxm0284), Anhui Provincial Education Department Key Project (KJ2019A0529), Fuyang Normal University Key Talent Project (rcxm201906), Key Project of Natural Science Research in Anhui University (KJ2019A0533), Fuyang Normal Teachers College Research Project (2014FSSK02ZD).
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Wang, Y. et al. (2021). Optimization and Performance Analysis of Extreme Learning Machine by L2-Norm Regularization. In: Abawajy, J., Choo, KK., Xu, Z., Atiquzzaman, M. (eds) 2020 International Conference on Applications and Techniques in Cyber Intelligence. ATCI 2020. Advances in Intelligent Systems and Computing, vol 1244. Springer, Cham. https://doi.org/10.1007/978-3-030-53980-1_60
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DOI: https://doi.org/10.1007/978-3-030-53980-1_60
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