Abstract
A Recurrent Trainable Neural Network (RTNN) with a two layer canonical architecture and a dynamic Backpropagation learning method are applied for local identification and local control of complex nonlinear plants. The RTNN model is incorporated in Hierarchical Fuzzy-Neural Multi-Model (HFNMM) architecture, combining the fuzzy model flexibility with the learning abilities of the RTNNs. A direct feedback/feedforward HFNMM control scheme using the states issued by the identification FNHMM is proposed. The proposed control scheme is applied for 1-DOF mechanical plant with friction, and the obtained results show that the control using HFNMM outperforms the fuzzy and the single RTNN one.
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Baruch, I., Olivares G., JL., Mariaca-Gaspar, CR., Guerra, R.G. (2007). A Fuzzy-Neural Hierarchical Multi-model for Systems Identification and Direct Adaptive Control. In: Melin, P., Castillo, O., Ramírez, E.G., Kacprzyk, J., Pedrycz, W. (eds) Analysis and Design of Intelligent Systems using Soft Computing Techniques. Advances in Soft Computing, vol 41. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72432-2_17
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DOI: https://doi.org/10.1007/978-3-540-72432-2_17
Publisher Name: Springer, Berlin, Heidelberg
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