Abstract
A Recurrent Trainable Neural Network (RTNN) with a two layer canonical architecture learned by a dynamic Backpropagation learning algorithm is incorporated in a Hierarchical Fuzzy-Neural Multi-Model (HFNMM) identifier, combining the fuzzy model flexibility with the learning abilities of the RTNNs. The local and global features of the proposed HFNMM identifier are implemented by a Hierarchical Sliding Mode Controller (HSMC). The proposed HSMC scheme is applied for 1-DOF mechanical plant with friction control, where the obtained comparative results show that the HSMC with a HFNMM identifier outperforms the SMC with a single RTNN identifier.
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Baruch, I., Guzman, JL.O., Mariaca-Gaspar, CR., Guerra, R.G. (2007). A Sliding Mode Control Using Fuzzy-Neural Hierarchical Multi-model Identifier. In: Castillo, O., Melin, P., Ross, O.M., Sepúlveda Cruz, R., Pedrycz, W., Kacprzyk, J. (eds) Theoretical Advances and Applications of Fuzzy Logic and Soft Computing. Advances in Soft Computing, vol 42. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72434-6_77
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DOI: https://doi.org/10.1007/978-3-540-72434-6_77
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