Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.12104/44923
Title: System identification using multilayer differential neural networks: A new result
Author: Del Castillo-Castro, T.
Castillo-Ortega, M.M.
Villarreal, I.
Brown, F.
Grijalva, H.
Perez-Tello, M.
Nuno-Donlucas, S.M.
Puig, J.E.
Issue Date: 2007
Abstract: The synthesis of composites of n-dodecylbenzene sulfonate-doped polyaniline (PANI-DBSA) and poly(styrene-metal acrylate) ionomers is presented. The ionomers of lithium, sodium and potassium were prepared by emulsion polymerization at different styrene-to-metal acrylate weight ratios. The composites made with the potassium ionomer exhibit the largest conductivity due to the higher content of acid groups that allows stronger interactions with the PANI chains compared to the Na and Li ionomers. IR spectroscopy suggests that hydrogen bonding interactions take place between PANI-DBSA chains and that amine salt groups form by chemical reactions between the amine groups of PANI and the acid groups of the ionomer. X-ray diffraction reveals that the ionomer affects the structural ordering of PANI-DBSA. All the PANI-DBSA-ionomer composites show higher thermal stability than the PANI-DBSA material. SEM shows a characteristic agglomerate morphology in all the composites. The composite showing the highest electrical conductivity was mixed with poly(n-butyl methacrylate) (PBMA) by extrusion and the films obtained have higher electrical conductivity than that of films of the same system without ionomer. " 2006 Elsevier Ltd. All rights reserved.",,,,,,"10.1016/j.compositesa.2006.02.001",,,"http://hdl.handle.net/20.500.12104/44923","http://www.scopus.com/inward/record.url?eid=2-s2.0-33751529781&partnerID=40&md5=eca79583cdf447774c181db1042f899a",,,,,,"2",,"Composites Part A: Applied Science and Manufacturing",,"639
645",,"38",,"Scopus
WOS",,,,,,"B. Electrical properties; B. Thermal properties; E. Extrusion",,,,,,"Synthesis and characterization of composites of DBSA-doped polyaniline and polystyrene-based ionomers",,"Article" "46734","123456789/35008",,"Pérez-Cruz, J.H., Centro Universitario de Ciencias Exactas e Ingenieras, Universidad de Guadalajara, Boulevard Marcelino Garca Barragn No. 1421, 44430 Guadalajara JAL, Mexico; Alanis, A.Y., Centro Universitario de Ciencias Exactas e Ingenieras, Universidad de Guadalajara, Boulevard Marcelino Garca Barragn No. 1421, 44430 Guadalajara JAL, Mexico; Rubio, J.D.J., Seccin de Estudios de Posgrado e Investigacin, ESIME-UA, IPN, Avenida de las Granjas No. 682, 02250 Santa Catarina NL, Mexico; Pacheco, J., Seccin de Estudios de Posgrado e Investigacin, ESIME-UA, IPN, Avenida de las Granjas No. 682, 02250 Santa Catarina NL, Mexico",,"Perez-Cruz, J.H.
Alanis, A.Y.
Rubio, J.D.J.
Pacheco, J.",,"2012",,"In previous works, a learning law with a dead zone function was developed for multilayer differential neural networks. This scheme requires strictly a priori knowledge of an upper bound for the unmodeled dynamics. In this paper, the learning law is modified in such a way that this condition is relaxed. By this modification, the tuning process is simpler and the dead-zone function is not required anymore. On the basis of this modification and by using a Lyapunov-like analysis, a stronger result is here demonstrated: the exponential convergence of the identification error to a bounded zone. Besides, a value for upper bound of such zone is provided. The workability of this approach is tested by a simulation example. Copyright " 2012 J. Humberto Pérez-Cruz et al.
URI: http://hdl.handle.net/20.500.12104/44955
http://www.scopus.com/inward/record.url?eid=2-s2.0-84861038833&partnerID=40&md5=287ed13c178d39a0e7fae2312596ce03
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