A Fuzzy-Neural Hierarchical Multi-model for systems identification and direct adaptive control

Ieroham Baruch; Jose Luis Olivares G.; Carlos Roman Mariaca-Gaspar; Rosalíba Galvan Guerra

doi:10.1007/978-3-540-72432-2_17

A Fuzzy-Neural Hierarchical Multi-model for systems identification and direct adaptive control

Ieroham Baruch, Jose Luis Olivares G., Carlos Roman Mariaca-Gaspar, Rosalíba Galvan Guerra

Producción científica: Capítulo del libro/informe/acta de congreso › Capítulo › revisión exhaustiva

5 Citas (Scopus)

Resumen

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.

Idioma original	Inglés
Título de la publicación alojada	Analysis and Design of Intelligent Systems using Soft Computing Techniques
Editores	Patricia Melin, Eduardo Gomez Ramirez, Janusz Kacprzyk, Witold Pedrycz
Páginas	163-172
Número de páginas	10
DOI	https://doi.org/10.1007/978-3-540-72432-2_17
Estado	Publicada - 2007
Publicado de forma externa	Sí

Serie de la publicación

Nombre	Advances in Soft Computing
Volumen	41
ISSN (versión impresa)	1615-3871
ISSN (versión digital)	1860-0794

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10.1007/978-3-540-72432-2_17

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Baruch, I., Olivares G., J. L., Mariaca-Gaspar, C. R., & Guerra, R. G. (2007). A Fuzzy-Neural Hierarchical Multi-model for systems identification and direct adaptive control. En P. Melin, E. Gomez Ramirez, J. Kacprzyk, & W. Pedrycz (Eds.), Analysis and Design of Intelligent Systems using Soft Computing Techniques (pp. 163-172). (Advances in Soft Computing; Vol. 41). https://doi.org/10.1007/978-3-540-72432-2_17

Baruch, Ieroham ; Olivares G., Jose Luis ; Mariaca-Gaspar, Carlos Roman et al. / A Fuzzy-Neural Hierarchical Multi-model for systems identification and direct adaptive control. Analysis and Design of Intelligent Systems using Soft Computing Techniques. editor / Patricia Melin ; Eduardo Gomez Ramirez ; Janusz Kacprzyk ; Witold Pedrycz. 2007. pp. 163-172 (Advances in Soft Computing).

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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.",

author = "Ieroham Baruch and {Olivares G.}, {Jose Luis} and Mariaca-Gaspar, {Carlos Roman} and Guerra, {Rosal{\'i}ba Galvan}",

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Baruch, I, Olivares G., JL, Mariaca-Gaspar, CR & Guerra, RG 2007, A Fuzzy-Neural Hierarchical Multi-model for systems identification and direct adaptive control. En P Melin, E Gomez Ramirez, J Kacprzyk & W Pedrycz (eds.), Analysis and Design of Intelligent Systems using Soft Computing Techniques. Advances in Soft Computing, vol. 41, pp. 163-172. https://doi.org/10.1007/978-3-540-72432-2_17

A Fuzzy-Neural Hierarchical Multi-model for systems identification and direct adaptive control. / Baruch, Ieroham; Olivares G., Jose Luis; Mariaca-Gaspar, Carlos Roman et al.
Analysis and Design of Intelligent Systems using Soft Computing Techniques. ed. / Patricia Melin; Eduardo Gomez Ramirez; Janusz Kacprzyk; Witold Pedrycz. 2007. p. 163-172 (Advances in Soft Computing; Vol. 41).

Producción científica: Capítulo del libro/informe/acta de congreso › Capítulo › revisión exhaustiva

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AB - 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 RG. A Fuzzy-Neural Hierarchical Multi-model for systems identification and direct adaptive control. En Melin P, Gomez Ramirez E, Kacprzyk J, Pedrycz W, editores, Analysis and Design of Intelligent Systems using Soft Computing Techniques. 2007. p. 163-172. (Advances in Soft Computing). doi: 10.1007/978-3-540-72432-2_17

A Fuzzy-Neural Hierarchical Multi-model for systems identification and direct adaptive control

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