Articles

 

Analyzing and forecasting the global CO₂ concentration - a collaborative fuzzy-neural agent network approach

 

T. Chen

 

Department of Industrial Engineering and Systems Management, Feng Chia University, Taichung City, Taiwan. E-mail address: tolychen@ms37.hinet.net

 

Abstract

In order to effectively analyze and forecast the global CO₂ concentration, a collaborative fuzzy-neural agent network is constructed in this study. In the collaborative fuzzy-neural agent network, a group of autonomous agents is used. These agents are programmed to analyze and forecast the global CO₂ concentration using the fuzzy back propagation network (FBPN) approach based on their local views. A collaboration mechanism is established to communicate the settings and forecasts of these agents, and to derive a single representative value from these forecasts using a radial basis function network. The real data were used to evaluate the effectiveness of the collaborative fuzzy-neural agent network approach.

Keywords: Global CO₂ concentration; Forecast; Collaborative intelligence; Agent; Fuzzy-neural network; Goal programming.

 

DESCARGAR ARTÍCULO EN FORMATO PDF

 

References

Ahmadaali, K., Liaghat, A., Haddad, O.B., & Heydari, N. (2013). Estimation of virtual water using support vector machine, K-nearest neighbour, and radial basis function neural network models. Int. J. Agronomy Plant Production, 4, 2926-2936.         [ Links ]

Babaei, A., Suratgar, A.A., & Salemi, A.H. (2013). Dimension Estimation of Rectangular Cracks Using Impedance Changes of the Eddy Current Probe with a Neural Network. Journal of Applied Research and Technology, 11, 397-401.         [ Links ]

Bhattacharya, A., & Vasant, P. (2007). Soft-sensing of level of satisfaction in TOC product-mix decision heuristic using robust fuzzy-LP. European Journal of Operational Research, 177, 55-70.         [ Links ]

Chen, T. (2008). A SOM-FBPN-ensemble approach with error feedback to adjust classification for wafer-lot completion time prediction. The International Journal of Advanced Manufacturing Technology, 37, 782-792.         [ Links ]

Chen, T. (2012). A collaborative fuzzy-neural system for global CO2 concentration forecasting. International Journal of Innovative Computing, Information and Control, 8, 7679-7696.         [ Links ]

Chen, T., & Wang, Y.C. (2011). A fuzzy-neural approach for global CO2 concentration forecasting. Intelligent Data Analysis, 15, 763-777.         [ Links ]

Chen, T., & Wang, Y.C. (2012). Long-term load forecasting by a collaborative fuzzy-neural approach. International Journal of Electrical Power & Energy Systems, 43, 454-464.         [ Links ]

Chen, T., & Wang, Y.C. (2014). An agent-based fuzzy collaborative intelligence approach for precise and accurate semiconductor yield forecasting. IEEE Transactions on Fuzzy Systems, 22, 201-211.         [ Links ]

CO2Now.org (2013). Accelerating rise of atmospheric CO2. Retrieved from: http://co2now.org/        [ Links ]

Earth System Research Laboratory Global Monitoring Division (2014). Trends in atmospheric carbon dioxide. Retrieved from: http://www.esrl.noaa.gov/gmd/ccgg/trends/        [ Links ]

Endo, T., Banno, A., & Tamura, Y. (2008, June). Research into sensor networks and Web APIs-Urban navigation systems utilising sensor network data. In: 5th International Conference on Networked Sensing Systems, 2008. INSS 2008 (pp. 166-169). IEEE.         [ Links ]

Eraslan, E. (2009). The estimation of product standard time by artificial neural networks in the molding industry. Mathematical Problems in Engineering, 2009, article ID 527452.         [ Links ]

Firoze, A., Arifin, M.S., & Rahman, R.M. (2013). Bangla user adaptive word Speech recognition: approaches and comparisons. International Journal of Fuzzy System Applications (IJFSA), 3, 1-36.         [ Links ]

Hong, W.C. (2011). Electric load forecasting by seasonal recurrent SVR with chaotic artificial bee colony algorithm. Energy, 36, 5568-5578.         [ Links ]

Lee, C.S., & Pan, C.Y. (2004). An intelligent fuzzy agent for meeting scheduling decision support system. Fuzzy Sets and Systems, 142, 467-488.         [ Links ]

López-Juárez, I., Castelán, M., Castro-Martínez, F.J., Peña-Cabrera, M., & Osorio-Comparan, R. (2013). Using Object's Contour, Form and Depth to Embed Recognition Capability into Industrial Robots. Journal of Applied Research and Technology, 11, 5-17.         [ Links ]

Lu, K.Y., & Sy, C.C. (2009). A real-time decision-making of maintenance using fuzzy agent. Expert Systems with Applications, 36, 2691-2698.         [ Links ]

Morreale, P.A. (2007, May). Wireless sensor network applications in urban telehealth. In: 21st International Conference on Advanced Information Networking and Applications Workshops, 2007, AINAW'07 (Vol. 2, pp. 810-814). IEEE.         [ Links ]

National Assessment Synthesis Team (2000). Climate change impacts on the United States: the potential consequences of climate variability and change. Washington DC: US Global Change Research Program.         [ Links ]

Pedrycz, W. (2008). Collaborative architectures of fuzzy modeling. Lecture Notes in Computer Science, 5050, 117-139.         [ Links ]

Peidro, D., & Vasant, P. (2011). Transportation planning with modified S-curve membership functions using an interactive fuzzy multi-objective approach. Applied Soft Computing, 11, 2656-2663.         [ Links ]

Uchimura, Y., Nasu, T., & Takahashi, M. (2007, September). Time synchronized wireless sensor network for vibration measurement. In: SICE, 2007 Annual Conference (pp. 2940-2945). IEEE.         [ Links ]

Wang, Q., Zhang, T., & Pettersson, S. (2008, March). An effort to understand the optimal routing performance in wireless sensor network. In: 22nd International Conference on Advanced Information Networking and Applications, 2008. AINA 2008 (pp. 279-286). IEEE.         [ Links ]

Wang, Q., Zhang, T., Pettersson, S. (2008). An effort to understand the optimal routing performance in wireless sensor network. Proceedings of the International Conference on Advanced Information Networking and Applications (pp. 279-286).         [ Links ]

Wrather, C., & Yu, P.L. (1982). Probability dominance in random outcomes. Journal of Optimization Theory and Applications, 36, 315-334.         [ Links ]

Yan, Z.Y., Zheng, B.Y., & Lin, Z.W. (2009). Research on opportunistic cooperation transmission and its performance in wireless sensor network. Journal of Electronics and Information Technology, 31, 215-218.         [ Links ]

Zarandi, M.F., Pourakbar, M., & Turksen, I.B. (2008). A fuzzy agent-based model for reduction of bullwhip effect in supply chain systems. Expert Systems with Applications, 34, 1680-1691.         [ Links ]