Abstract
This paper compares the performance by using constrained learning algorithm (CLA) and recursive least square algorithm (RLSA) to solve linear simultaneous equations. It was found in experiments that the convergent speed for this CLA is much faster than the recursive least square back propagation (RLS-BP) algorithm. Finally, related experimental results are presented.
This work was supported by NSF of China and the Grant of “Hundred Talents Program" of Chinese Academy of Sciences of China.
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Huang, DS. (2003). On the Comparisons between RLSA and CLA for Solving Arbitrary Linear Simultaneous Equations. In: Liu, J., Cheung, Ym., Yin, H. (eds) Intelligent Data Engineering and Automated Learning. IDEAL 2003. Lecture Notes in Computer Science, vol 2690. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45080-1_24
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DOI: https://doi.org/10.1007/978-3-540-45080-1_24
Publisher Name: Springer, Berlin, Heidelberg
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