Abstract
Iterative learning control problem based on improved discrete-time Hopfield neural networks is considered in this paper. For the every process of iterative learning control, the neural networks execute a cycle that includes variable terms of learning time and training iterative number. The iterative learning control with improved Hopfield neural networks is formulated that can be described as a two-dimensional (2-D) Roesser model with variable coefficients. In terms of 2-D systems theory, sufficient conditions that iterative learning error approaches to zero are given. It has been shown that convergence of iterative learning control problem based on Hopfield neural networks is derived by 2-D systems theory instead of conventional algorithms that minimize a cost function.
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Kang, J., Tang, W. (2007). Iterative Learning Control Analysis Based on Hopfield Neural Networks. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4493. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72395-0_21
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DOI: https://doi.org/10.1007/978-3-540-72395-0_21
Publisher Name: Springer, Berlin, Heidelberg
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