Computer Science – Neural and Evolutionary Computing
Scientific paper
2010-04-12
Intelligent Automation and Soft Computing, Vol. 17, No. 2, pp. 133-147, 2011
Computer Science
Neural and Evolutionary Computing
15 pages, 5 figures
Scientific paper
Back-propagation with gradient method is the most popular learning algorithm for feed-forward neural networks. However, it is critical to determine a proper fixed learning rate for the algorithm. In this paper, an optimized recursive algorithm is presented for online learning based on matrix operation and optimization methods analytically, which can avoid the trouble to select a proper learning rate for the gradient method. The proof of weak convergence of the proposed algorithm also is given. Although this approach is proposed for three-layer, feed-forward neural networks, it could be extended to multiple layer feed-forward neural networks. The effectiveness of the proposed algorithms applied to the identification of behavior of a two-input and two-output non-linear dynamic system is demonstrated by simulation experiments.
Bajic Vladimir B.
Sha Daohang
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