Abstract
This paper proposes an improved learning algorithm based on generalized SOM for dynamical non-linear system identification. To improve the convergent speed and the accuracy of SOM algorithm, we propose the improved self-organizing algorithm, which, at first, applies the multiple local models instead of the global model, and secondly, adjusts the weights of the computing output layers along with the weights of the competing neurons layer during the training process. We prove that the improved algorithm is convergent if the network has suitable initial weights and small positive real parameters. The simulation results using our improved generalized SOM show an improvement for non-linear system compared to traditional neural network control systems.
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© 2006 Springer-Verlag Berlin Heidelberg
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Zhang, K., Guan, GZ., Chen, FF., Zhang, L., Du, ZY. (2006). Improved Learning Algorithm Based on Generalized SOM for Dynamic Non-linear System. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3971. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11759966_88
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DOI: https://doi.org/10.1007/11759966_88
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34439-1
Online ISBN: 978-3-540-34440-7
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