Abstract
In this paper, an improved training algorithm based on the terminal attractor concept for feedforward neural network learning is proposed. A condition to avoid the singularity problem is proposed. The effectiveness of the proposed algorithm is evaluated by various simulation results for a function approximation problem and a stock market index prediction problem. It is shown that the terminal attractor based training algorithm performs consistently in comparison with other existing training algorithms.
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Yu, X., Wang, B., Batbayar, B. et al. An improved training algorithm for feedforward neural network learning based on terminal attractors. J Glob Optim 51, 271–284 (2011). https://doi.org/10.1007/s10898-010-9597-6
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DOI: https://doi.org/10.1007/s10898-010-9597-6