A Fast and Accurate Online Sequential Learning Algorithm for Feedforward Networks

作者: Nan-Ying Liang , Guang-Bin Huang , P. Saratchandran , N. Sundararajan

DOI: 10.1109/TNN.2006.880583

关键词:

摘要: In this paper, we develop an online sequential learning algorithm for single hidden layer feedforward networks (SLFNs) with additive or radial basis function (RBF) hidden nodes in a …

参考文章(34)
Hai-Jun Rong, Paramasivan Saratchandran, Guang-Bin Huang, Nan-Ying Liang, Narasimhan Sundararajan, On-Line Sequential Extreme Learning Machine computational intelligence. pp. 232- 237 ,(2005)
L.S.H. Ngia, J. Sjoberg, M. Viberg, Adaptive neural nets filter using a recursive Levenberg-Marquardt search direction asilomar conference on signals, systems and computers. ,vol. 1, pp. 697- 701 ,(1998) , 10.1109/ACSSC.1998.750952
Visakan Kadirkamanathan, Mahesan Niranjan, A function estimation approach to sequential learning with neural networks Neural Computation. ,vol. 5, pp. 954- 975 ,(1993) , 10.1162/NECO.1993.5.6.954
Gene H. Golub, Charles F. Van Loan, Matrix computations (3rd ed.) Johns Hopkins University Press. ,(1996)
Lu Yingwei, N. Sundararajan, P. Saratchandran, A sequential learning scheme for function approximation using minimal radial basis function neural networks Neural Computation. ,vol. 9, pp. 461- 478 ,(1997) , 10.1162/NECO.1997.9.2.461
Edwin KP Chong, Stanislaw H Żak, An introduction to optimization ,(1996)
C. L. Blake, UCI Repository of machine learning databases www.ics.uci.edu/〜mlearn/MLRepository.html. ,(1998)
M. Mackey, L Glass, Oscillation and chaos in physiological control systems Science. ,vol. 197, pp. 287- 289 ,(1977) , 10.1126/SCIENCE.267326
Lu Yingwei, N. Sundararajan, P. Saratchandran, Performance evaluation of a sequential minimal radial basis function (RBF) neural network learning algorithm IEEE Transactions on Neural Networks. ,vol. 9, pp. 308- 318 ,(1998) , 10.1109/72.661125
Guang-Bin Huang, Yan-Qiu Chen, H.A. Babri, Classification ability of single hidden layer feedforward neural networks IEEE Transactions on Neural Networks. ,vol. 11, pp. 799- 801 ,(2000) , 10.1109/72.846750