• Interested in doing a undergraduate research (Iniciação Científica)? Take a look here (html)
• Visit my research group web page for my details www.ufsj.edu.br/gcom
• Topics of research: Computer Arithmetic, System Identification, Chaos, Complex Systems
• Analysis and Modelling of Systems
• • Analysis of nonlinear dynamic systems for description and validation.
  • Development models for systems and processes in general and control applications.
  • Use of  non-linear representations: NARMAX, RNN (Random Neural Networks) and model based on individuals (MBI).
  • Application of  and stochastic multiobjective optimization in system identification. 5) Systems of interest: electronics, mechatronics; electric power; electromagnetic, biological, economic, social and epidemiological.
  • From 2014, emphasis has been given to the mathematical rigor with which models are simulated, identified and validated. To this has been used mathematical analysis tools and understanding of how the computer arithmetic plays an important role in describing (and not describing) dynamic systems. In this topic see recent publications:
    • Nepomuceno, E. G. Convergence of recursive functions on computers The Journal of Engineering, Institution of Engineering and Technology, 2014, 1-3 (doi)
    • Nepomuceno E. G. and Martins S. A. M.  (2016), "A lower bound error for free-run simulation of the polynomial NARMAX", Systems Science & Control Engineering. Vol. 4(1), pp. 50-58. [doi]
    • Mendes E. M. A. M. and Nepomuceno E. G. (2016), "A very simple method to calculate the (positive) Largest Lyapunov Exponent using interval extensions", International Journal of Bifurcation and Chaos in Applied Sciences and Engineering. (Accepted)
    •  
• You can follow his publications and citations on the Google Scholar page.
• Selected publications
  1. Nepomuceno E. G. and Martins S. A. M.  (2016), "A lower bound error for free-run simulation of the polynomial NARMAX", Systems Science & Control Engineering. Vol. 4(1), pp. 50-58. [doi]
  2. NEPOMUCENO, E. G. . Convergence of recursive functions on computers. The Journal
     of Engineering, v. 1, p. 1-3, 2014.
  3. NEPOMUCENO, E. G. . Multiobjective learning in the random neural network. International Journal of Advanced Intelligence Paradigms, v. 6, p. 66-80, 2014.
  4. RIVERET, REGIS, ARTIKIS, ALEXANDER, PITT, JEREMY, NEPOMUCENO, ERIVELTON G. Self-Governance by Transfiguration: From Learning to Prescription Changes In: 2014 IEEE 8th International Conference on SelfAdaptive and SelfOrganizing Systems (SASO), 2014, London. 2014 IEEE Eighth International Conference on Self-Adaptive and Self-Organizing Systems. IEEE, 2014. p.70
  5. BARBOSA, A. M. ; NEPOMUCENO, E. G. ; BARROSO, M. F. S. ; PERILO, T. V. C. ; CASAS, E. B. L. . Automation of the process for accessing lip forces. Journal of the Brazilian Society of Mechanical Sciences and Engineering (Impresso), p. 1-9, 2014.
  6. NEPOMUCENO, E. G.Structure Optimization of the Recurrent Random Neural Network In: XX Congresso Brasileiro de Automática, 2014, Belo Horizonte. Anais do XX Congresso Brasileiro de Automática. , 2014.
  7. MARTINS, SAMIR ANGELO MILANI, BARBOSA, A. M., NEPOMUCENO, E. G. Determinação do Número de Termos de Modelos Polinomiais: O Critério de Informação de Akaike Multiobjetivo In: XI Simpósio Brasileiro de Automação Inteligente, 2013, Fortaleza. Anais do XI Simpósio Brasileiro de Automação Inteligente. , 2013.
  8. MACEDO-VITOR, A. D., Moraes, E. K. A. L., NEPOMUCENO, E. G. Modelos Compartimentais Investigados por Processos Estocásticos In: XI Simpósio Brasileiro de Automação Inteligente, 2013, Fortaleza. Anais do XI Simpósio Brasileiro de Automação Inteligente. , 2013.
  9. MARTINS, SAMIR ANGELO MILANI ; NEPOMUCENO, E. G. ; BARROSO, M. F. S.. Improved Structure Detection For Polynomial NARX Models Using a Multiobjective Error Reduction Ratio. Journal of Control, Automation and Electrical Systems, v. 24, p. 764-772, 2013.
  10. TELES, F. ; QUEIROZ E MELO, M. F. A ; NEPOMUCENO, E. G. . Reflexões Sobre a Utilização de um Videogame Multiplayer para Investigar o Fenômeno da Cooperação em Psicologia Social. Pesquisas e Práticas Psicossociais, v. 6, p. 287-297, 2012.
  11. LACERDA, M. J., TEIXEIRA, W. W. M., NEPOMUCENO, E. G. Agents allocation for control of epidemics by means genetic algorithm (in Portuguese). Biomatemática (UNICAMP). , v.20, p.79 - 92, 2010.
  12. SILVA, L. F. P. ; LEITE, V. J. S. ; MIRANDA, M. F. ; NEPOMUCENO, E. G. . Robust D-stabilization with minimization of the H-infinity guaranteed cost for uncertain discretetime systems with multiple delays in the state. Atlanta - USA. Proceedings of 49th IEEE Conference on Decision and Control, 2010. p. 1-7.
  13. VIEIRA, J. P. ; LACERDA, M. J. ; MARTINS, S. A. M. ; NEPOMUCENO, E. G. ; CAETANO, S. M. . Individual Based Models applied for Computer Virus Propagations: A Case Study for heterogeneous meta-populations (in Portuguese). In: XVIII Congresso Brasileiro de Automática, 2010, Bonito - MS. 1446-1453.
  14. MARTINS, S. A. M., BRAGA, D. C. S., NEPOMUCENO, E. G., GOMES, T. V., REIS, M. L. F. Investigation of static curve information for multiobjective system identification. Journal of Computational Interdisciplinary Sciences. v.1, p.149 - 157, 2009.
  15. TELES, F., QUEIROZ E MELO, M. F. A, NEPOMUCENO, E. G. Interdisciplinarity in Focus: Possibilities in a Prey-Predator Dynamics. Dynamics of Continuous, Discrete and Impulsive Systems. B, Applications and Algorithms. , v.14, p.598 - 602, 2007.
  16. NEPOMUCENO, E. G., TAKAHASHI, R. H. C., AGUIRRE, L. A. Multiobjective parameter estimation for nonlinear systems: Affine information and least-squares formulation. International Journal of Control, v.80, p.863 - 871, 2007
  17. NEPOMUCENO, E. G., TAKAHASHI, R. H. C., AMARAL, G. F. V., AGUIRRE, L. A. Nonlinear Identification Using Prior Knowledge of Fixed Points: a Multiobjective Approach. International Journal of Bifurcation and Chaos in Applied Sciences and Engineering , v.13, p.1229 - 1246, 2003.

Última atualização: 22/11/2016