Análise de uma Rede Neural Híbrida como base para um Mecanismo de Predição de Situação
Resumo
Este artigo possui como foco apresentar os resultados da busca de uma técnica que possa ser utilizada como base em um mecanismo de predição de Situação voltado para Inteligência Ambiental. Foi analisado o uso de uma rede neural híbrida chamada de Multioutput Adaptative Neural Fuzzy Inference System (MANFIS) e então sua capacidade preditiva foi comparada com uma rede Multi Layer Perceptron (MLP). Os resultados demonstram que dependendo do tipo de aplicação o uso de redes neurais pode ser considerado uma boa alternativa para predição de situação quando combinadas com outras técnicas.
Referências
Abraham, A. (2005). Adaptation of Fuzzy Inference System Using Neural Learning. Architecture, 83, 5383.
Alves Lino, J., Salem, B., & Rauterberg, M. (2010). Responsive environments: User experiences for ambient intelligence. Journal of Ambient, 2(4), 347–367. IOS Press. DOI: 10.3233/AIS-2010-0080
Anagnostopoulos, T., Anagnostopoulos, C. B., Hadjiefthymiades, S., Kalousis, A., & Kyriakakos, M. (2007). Path Prediction through Data Mining. Artificial Intelligence, 128-135. IEEE.
Anagnostopoulos, T., Anagnostopoulos, C., & Hadjiefthymiades, S. (2009). An Online Adaptive Model for Location Prediction. Autonomics’09 (pp. 64-78).
Anagnostopoulos, T., Anagnostopoulos, C., & Hadjiefthymiades, S. (2011). An adaptive location prediction model based on fuzzy control. Computer Communications, 34(7), 816-834.
Anagnostopoulos, T., Anagnostopoulos, C., Hadjiefthymiades, S., Kyriakakos, M., & Kalousis, A. (2009). Predicting the location of mobile users: a machine learning approach. Proceedings of the 2009 international conference on Pervasive services (pp. 65-72). ACM. DOI: 10.1145/1568199.1568210
Augusto, Juan Carlos, & Shapiro, D. (2007). Notes from editors. Frontiers in Artificial Intelligence and Applications, 164.
Benmiloud, T. (2010). Multioutput adaptive neuro-fuzzy inference system. In 11th WSEAS international conference on neural networks (pp. 94-98). Stevens Point, Wisconsin, USA: World Scientific and Engineering Academy and Society (WSEAS).
Benta, K.-I., Cremene, M., & Todica, V. (2009). Towards an Affective Aware Home. Proceedings of the ICOST (pp. 74-81).
Benta, K.-I., Hoszu, A., Văcariu, L., & Creţ, O. (2009). Agent based smart house platform with affective control. Proceedings of the 2009 Euro American Conference on Telematics and Information Systems New Opportunities to increase Digital Citizenship, 1-7. ACM Press. DOI: 10.1145/1551722.1551740
Benta, K.-iulian, Cremene, M., Hoszu, A., Editors, M., Margaria, T., Padberg, J., & Taentzer, G. (2010). Training the Behaviour Preferences on Context Changes. ECEASST, 28.
Bettini, C., Brdiczka, O., Henricksen, K., Indulska, J., Nicklas, D., Ranganathan, A., & Riboni, D. (2010). A survey of context modelling and reasoning techniques. Pervasive and Mobile Comp., 6(2), 161-180.
Buchmayr, M., & Kurschl, W. (2011). A survey on situation-aware ambient intelligence systems. Journal of Ambient Intelligence and Humanized Computing, 2(3), 175-183. DOI: 10.1007/s12652-011-0055-1
Cook, D. J., Augusto, J. C., & Jakkula, V. R. (2009). Ambient intelligence: applications in society and opportunities for artificial intelligence. Pervasive and Mobile Computing.
Costa, C. A. (2008). Continuum: A Context-aware Service-based Software Infrastructure for Ubiquitous Computing. UFRGS.
Dey, A. K. (2001). Understanding and Using Context. (A. K. Dey, G. Kortüm, D. R. Morse, & A. Schmidt, Eds.)Personal and Ubiquitous Computing, 5(1), 4-7. Springer-Verlag. DOI: 10.1007/s007790170019
Elmahalawy, A. M., Elfishawy, N., & El-dien, M. N. (2010). Anticipation the consumed electrical power in Smart Home using evolutionary algorithms. 2010 International Conference on Multimedia Computing and Information Technology MCIT, 81-84. Ieee. DOI: 10.1109/MCIT.2010.5444847
Endsley, M. R. (2006). Situation Awareness. In G. Salvendy (Ed.), Handbook of Human Factors and Ergonomics (pp. 528-542). John Wiley & Sons Ltd. DOI: 10.1002/0470048204.ch20
Endsley, M. R., & Connors, E. S. (2008). Situation Awareness: State of the Art. Energy, 13-16.
Gellersen, H., Schmidt, A., & Beigl, M. (2002). Multi-Sensor Context-Awareness in Mobile Devices and Smart Artefacts. Mobile Networks and Applications, 7(5), 341-351.
Kluwer Academic Publishers Haykin, S. (2011). Redes Neurais Principios e práticas (2nd ed.).
Jang, J. S. R. (1993). ANFIS: adaptive-network-based fuzzy inference system. Ieee Transactions On Systems Man And Cybernetics, 23(3), 665-685. IEEE. DOI: 10.1109/21.256541
Kasteren, T. L. M., Englebienne, G., & Kröse, B. J. a. (2010). An activity monitoring system for elderly care using generative and discriminative models. Personal and Ubiquitous Computing, 14(6), 489-498.
Leithardt, V. R. Q. Geyer, C. F. R., Nunes, D., Silva, S. J.;, Rolim, C. O., Rosseto, A. G. de M., Dantas, M. A. . (2012). Percontrol A Pervasive System for Educational Environments using Wireless Sensor Networks. International Conference on Computing, Networking and Comm. (ICNC 2012).
Mackey, M. C., & Glass, L. (1977). Oscillation and chaos in physiological control systems. Science, 197(4300), 287-289. DOI: 10.1126/science.267326
Marza, V., & Teshnehlab, M. (2009). Estimating Development Time and Effort of Software Projects by using a Neuro_Fuzzy Approach. In K. Jayanthakumaran (Ed.), Advanced Technologies (pp. 395-412).
Ramos, C., Augusto, J. C., & Shapiro, D. (2008). Ambient intelligence: The next step for artificial intelligence. IEEE Intelligent Systems, 23(2), 15–18.
Yamin, A. C. (2004). Arquitetura para um Ambiente de Grade Computacional Direcionada às Aplicações Distribuídas, Móveis e Conscientes de Contexto da Computação Pervasiva. UUFRGS.
Yau, S. S., Huang, D., Gong, H., & Science, C. (2004). Development and Runtime Support for Situation-Aware Application Software in Ubiquitous Computing Environments. Computer.
Yau, S. S., Wang, Y., Karim, F., & Science, C. (2002). Development of Situation-Aware Application Software for Ubiquitous Computing Environments. Computer.
Ye, J., Dobson, S., & McKeever, S. (2011). A review of situation identification techniques in pervasive computing. Pervasive and Mobile Computing, In Press,(0). Elsevier B.V.
Alves Lino, J., Salem, B., & Rauterberg, M. (2010). Responsive environments: User experiences for ambient intelligence. Journal of Ambient, 2(4), 347–367. IOS Press. DOI: 10.3233/AIS-2010-0080
Anagnostopoulos, T., Anagnostopoulos, C. B., Hadjiefthymiades, S., Kalousis, A., & Kyriakakos, M. (2007). Path Prediction through Data Mining. Artificial Intelligence, 128-135. IEEE.
Anagnostopoulos, T., Anagnostopoulos, C., & Hadjiefthymiades, S. (2009). An Online Adaptive Model for Location Prediction. Autonomics’09 (pp. 64-78).
Anagnostopoulos, T., Anagnostopoulos, C., & Hadjiefthymiades, S. (2011). An adaptive location prediction model based on fuzzy control. Computer Communications, 34(7), 816-834.
Anagnostopoulos, T., Anagnostopoulos, C., Hadjiefthymiades, S., Kyriakakos, M., & Kalousis, A. (2009). Predicting the location of mobile users: a machine learning approach. Proceedings of the 2009 international conference on Pervasive services (pp. 65-72). ACM. DOI: 10.1145/1568199.1568210
Augusto, Juan Carlos, & Shapiro, D. (2007). Notes from editors. Frontiers in Artificial Intelligence and Applications, 164.
Benmiloud, T. (2010). Multioutput adaptive neuro-fuzzy inference system. In 11th WSEAS international conference on neural networks (pp. 94-98). Stevens Point, Wisconsin, USA: World Scientific and Engineering Academy and Society (WSEAS).
Benta, K.-I., Cremene, M., & Todica, V. (2009). Towards an Affective Aware Home. Proceedings of the ICOST (pp. 74-81).
Benta, K.-I., Hoszu, A., Văcariu, L., & Creţ, O. (2009). Agent based smart house platform with affective control. Proceedings of the 2009 Euro American Conference on Telematics and Information Systems New Opportunities to increase Digital Citizenship, 1-7. ACM Press. DOI: 10.1145/1551722.1551740
Benta, K.-iulian, Cremene, M., Hoszu, A., Editors, M., Margaria, T., Padberg, J., & Taentzer, G. (2010). Training the Behaviour Preferences on Context Changes. ECEASST, 28.
Bettini, C., Brdiczka, O., Henricksen, K., Indulska, J., Nicklas, D., Ranganathan, A., & Riboni, D. (2010). A survey of context modelling and reasoning techniques. Pervasive and Mobile Comp., 6(2), 161-180.
Buchmayr, M., & Kurschl, W. (2011). A survey on situation-aware ambient intelligence systems. Journal of Ambient Intelligence and Humanized Computing, 2(3), 175-183. DOI: 10.1007/s12652-011-0055-1
Cook, D. J., Augusto, J. C., & Jakkula, V. R. (2009). Ambient intelligence: applications in society and opportunities for artificial intelligence. Pervasive and Mobile Computing.
Costa, C. A. (2008). Continuum: A Context-aware Service-based Software Infrastructure for Ubiquitous Computing. UFRGS.
Dey, A. K. (2001). Understanding and Using Context. (A. K. Dey, G. Kortüm, D. R. Morse, & A. Schmidt, Eds.)Personal and Ubiquitous Computing, 5(1), 4-7. Springer-Verlag. DOI: 10.1007/s007790170019
Elmahalawy, A. M., Elfishawy, N., & El-dien, M. N. (2010). Anticipation the consumed electrical power in Smart Home using evolutionary algorithms. 2010 International Conference on Multimedia Computing and Information Technology MCIT, 81-84. Ieee. DOI: 10.1109/MCIT.2010.5444847
Endsley, M. R. (2006). Situation Awareness. In G. Salvendy (Ed.), Handbook of Human Factors and Ergonomics (pp. 528-542). John Wiley & Sons Ltd. DOI: 10.1002/0470048204.ch20
Endsley, M. R., & Connors, E. S. (2008). Situation Awareness: State of the Art. Energy, 13-16.
Gellersen, H., Schmidt, A., & Beigl, M. (2002). Multi-Sensor Context-Awareness in Mobile Devices and Smart Artefacts. Mobile Networks and Applications, 7(5), 341-351.
Kluwer Academic Publishers Haykin, S. (2011). Redes Neurais Principios e práticas (2nd ed.).
Jang, J. S. R. (1993). ANFIS: adaptive-network-based fuzzy inference system. Ieee Transactions On Systems Man And Cybernetics, 23(3), 665-685. IEEE. DOI: 10.1109/21.256541
Kasteren, T. L. M., Englebienne, G., & Kröse, B. J. a. (2010). An activity monitoring system for elderly care using generative and discriminative models. Personal and Ubiquitous Computing, 14(6), 489-498.
Leithardt, V. R. Q. Geyer, C. F. R., Nunes, D., Silva, S. J.;, Rolim, C. O., Rosseto, A. G. de M., Dantas, M. A. . (2012). Percontrol A Pervasive System for Educational Environments using Wireless Sensor Networks. International Conference on Computing, Networking and Comm. (ICNC 2012).
Mackey, M. C., & Glass, L. (1977). Oscillation and chaos in physiological control systems. Science, 197(4300), 287-289. DOI: 10.1126/science.267326
Marza, V., & Teshnehlab, M. (2009). Estimating Development Time and Effort of Software Projects by using a Neuro_Fuzzy Approach. In K. Jayanthakumaran (Ed.), Advanced Technologies (pp. 395-412).
Ramos, C., Augusto, J. C., & Shapiro, D. (2008). Ambient intelligence: The next step for artificial intelligence. IEEE Intelligent Systems, 23(2), 15–18.
Yamin, A. C. (2004). Arquitetura para um Ambiente de Grade Computacional Direcionada às Aplicações Distribuídas, Móveis e Conscientes de Contexto da Computação Pervasiva. UUFRGS.
Yau, S. S., Huang, D., Gong, H., & Science, C. (2004). Development and Runtime Support for Situation-Aware Application Software in Ubiquitous Computing Environments. Computer.
Yau, S. S., Wang, Y., Karim, F., & Science, C. (2002). Development of Situation-Aware Application Software for Ubiquitous Computing Environments. Computer.
Ye, J., Dobson, S., & McKeever, S. (2011). A review of situation identification techniques in pervasive computing. Pervasive and Mobile Computing, In Press,(0). Elsevier B.V.
Publicado
16/07/2012
Como Citar
ROLIM, Carlos O.; ROSSETTO, Anubis; LEITHARDT, Valderi R. Q.; GEYER, Cláudio F. R..
Análise de uma Rede Neural Híbrida como base para um Mecanismo de Predição de Situação. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO UBÍQUA E PERVASIVA (SBCUP), 4. , 2012, Curitiba/PR.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2012
.
p. 11-20.
ISSN 2595-6183.
