Abstract
The self-organizing map (SOM) is an unsupervised neural network which projects high-dimensional data onto a low-dimensional. A novel model based on interval self-organizing map(ISOM) whose weights are interval numbers presented in this paper differ from conventional SOM approach. Correspondingly, a new competition algorithm based on gradient descent algorithm is proposed according to a different criterion function defined in this paper, and the convergence of the new algorithm is proved. To improve the robustness of inverse control system, the inverse controller is approximated by ISOM which is cascaded with the original to capture composite pseudo-linear system. Simulation results show that the inverse system has superior performance of tracking precision and robustness.
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Liu, L., Xiao, J., Yu, L. (2008). Interval Self-Organizing Map for Nonlinear System Identification and Control. In: Sun, F., Zhang, J., Tan, Y., Cao, J., Yu, W. (eds) Advances in Neural Networks - ISNN 2008. ISNN 2008. Lecture Notes in Computer Science, vol 5263. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87732-5_10
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DOI: https://doi.org/10.1007/978-3-540-87732-5_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-87731-8
Online ISBN: 978-3-540-87732-5
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