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Learning Structures of Conceptual Models from Observed Dynamics Using Evolutionary Echo State Networks


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[1] J. D. Sterman, Business Dynamics: Systems Thinking and Modeling for a Complex World, vol. 19. Irwin/McGraw-Hill Boston, 2000.Search in Google Scholar

[2] F. C. Billari, Agent-based computational modelling: applications in demography, social, economic and environmental sciences. Taylor & Francis, 2006.Search in Google Scholar

[3] R. A. Howard and J. E. Matheson, Influence diagrams, Decis. Anal., vol. 2, no. 3, pp. 127–143, 2005.10.1287/deca.1050.0020Search in Google Scholar

[4] F.-R. Lin, M.-C. Yang, and Y.-H. Pai, A generic structure for business process modeling, Bus. Process Manag. J., vol. 8, no. 1, pp. 19–41, 2002.10.1108/14637150210418610Search in Google Scholar

[5] L. Schruben, Simulation modeling with event graphs, Commun. ACM, vol. 26, no. 11, pp. 957–963, 1983.10.1145/182.358460Open DOISearch in Google Scholar

[6] S. Robinson, Simulation: the practice of model development and use. Palgrave Macmillan, 2014.Search in Google Scholar

[7] J. Ryan and C. Heavey, Requirements gathering for simulation, in Proceedings of the 3rd Operational Research Society Simulation Workshop. The Operational Research Society, Birmingham, UK, 175-184, 2006.Search in Google Scholar

[8] A. Medina-Borja and K. S. Pasupathy, Uncovering complex relationships in system dynamics modeling: Exploring the use of CART, CHAID and SEM, in Proceedings of the 25th International Conference of the System Dynamics Society, (Boston, USA), pp. 1–24, 2007.Search in Google Scholar

[9] V. Quiñones-Avila and A. Medina-Borja, Universal healthcare: key behavioural factors affecting providers and recipients value propositions: a structural causal model of the puerto rico experience, Int. J. of Behav. and Hlthc. Res., vol. 3, no. 1, pp. 25–45, 2012.10.1504/IJBHR.2012.045618Search in Google Scholar

[10] M. Drobek, W. Gilani, T. Molka, and D. Soban, Automated equation formulation for causal loop diagrams, Lecture Notes in Business Information Processing, vol. 208, pp. 38–49, 2015.10.1007/978-3-319-19027-3_4Search in Google Scholar

[11] E. Pruyt, S. Cunningham, J. Kwakkel, and J. De Bruijn, From data-poor to data-rich: system dynamics in the era of big data, in Proceedings of the 32nd International Conference of the System Dynamics Society, Delft, The Netherlands, 20-24 July 2014.Search in Google Scholar

[12] H. Jaeger, The ’echo state’ approach to analysing and training recurrent neural networks-with an erratum note, Bonn, Germany: German National Research Center for Information Technology GMD Technical Report, vol. 148, p. 34, 2001.Search in Google Scholar

[13] H. Abdelbari and K. Shafi, Learning causal loop diagram-like structures for system dynamics modeling using echo state networks, Syst. Dynam. Rev. - In Press, 2017.Search in Google Scholar

[14] D. E. Goldberg, Genetic algorithms. Pearson Education India, 2006.Search in Google Scholar

[15] R. Storn and K. Price, Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces, J. Global. Optim., vol. 11, no. 4, pp. 341–359, 1997.10.1023/A:1008202821328Open DOISearch in Google Scholar

[16] J. Kennedy, Particle swarm optimization, in Encyclopedia of machine learning, pp. 760–766, Springer, 2011.10.1007/978-0-387-30164-8_630Search in Google Scholar

[17] Z. Wang, J. Zhang, J. Ren, and M. N. Aslam, A geometric singular perturbation approach for planar stationary shock waves, Physica D, vol. 310, pp. 19–36, 2015.10.1016/j.physd.2015.04.004Search in Google Scholar

[18] C. K. Jones, R. Marangell, P. D. Miller, and R. G. Plaza, On the stability analysis of periodic sine–gordon traveling waves, Physica D, vol. 251, pp. 63–74, 2013.10.1016/j.physd.2013.02.003Search in Google Scholar

[19] V. V. Gursky, J. Reinitz, and A. M. Samsonov, How gap genes make their domains: An analytical study based on data driven approximations, Chaos, vol. 11, no. 1, pp. 132–141, 2001.10.1063/1.134989012779448Search in Google Scholar

[20] P. Young, Data-based mechanistic modelling of environmental, ecological, economic and engineering systems, Environ. Modell. Softw., vol. 13, no. 2, pp. 105–122, 1998.10.1016/S1364-8152(98)00011-5Search in Google Scholar

[21] Y. Zhao, T. Weng, and M. Small, Response of the parameters of a neural network to pseudoperiodic time series, Physica D, vol. 268, pp. 79–90, 2014.10.1016/j.physd.2013.11.002Search in Google Scholar

[22] Y. Feng, Y. Liu, X. Tong, M. Liu, and S. Deng, Modeling dynamic urban growth using cellular automata and particle swarm optimization rules, Landscape Urban Plan., vol. 102, no. 3, pp. 188–196, 2011.10.1016/j.landurbplan.2011.04.004Open DOISearch in Google Scholar

[23] N. Petrov and A. Gegov, Model optimization for complex systems using fuzzy networks theory, in Proceedings of the 8th WSEAS international conference on Artificial intelligence, knowledge engineering and data bases, pp. 116–121, World Scientific and Engineering Academy and Society (WSEAS), 2009.Search in Google Scholar

[24] I. M. Greca and M. A. Moreira, Mental models, conceptual models, and modelling, Int. J. Sci. Educ, vol. 22, no. 1, pp. 1–11, 2000.10.1080/095006900289976Search in Google Scholar

[25] J. D. Sterman, Systems dynamics modeling: tools for learning in a complex world, IEEE Eng. Manag. Rev., vol. 30, no. 1, pp. 42–42, 2002.10.1109/EMR.2002.1022404Search in Google Scholar

[26] G. Desthieux, F. Joerin, and M. Lebreton, Ulysse: a qualitative tool for eliciting mental models of complex systems, Syst. Dynam. Rev., vol. 26, no. 2, pp. 163–192, 2010.10.1002/sdr.434Open DOISearch in Google Scholar

[27] K.-i. Funahashi and Y. Nakamura, Approximation of dynamical systems by continuous time recurrent neural networks, Neural networks, vol. 6, no. 6, pp. 801–806, 1993.10.1016/S0893-6080(05)80125-XSearch in Google Scholar

[28] H. Jaeger, Tutorial on training recurrent neural networks, covering BPPT, RTRL, EKF and the” echo state network” approach, Tech. Rep. 159, Fraunhofer Institute for Autonomous Intelligent Systems (AIS), 2002b.Search in Google Scholar

[29] D. Koryakin, J. Lohmann, and M. V. Butz, Balanced echo state networks, Neural Networks, vol. 36, pp. 35–45, 2012.10.1016/j.neunet.2012.08.00823037774Open DOISearch in Google Scholar

[30] I. B. Yildiz, H. Jaeger, and S. J. Kiebel, Re-visiting the echo state property, Neural networks, vol. 35, pp. 1–9, 2012.10.1016/j.neunet.2012.07.00522885243Search in Google Scholar

[31] M. Lukoševišius, A practical guide to applying echo state networks, in Neural Networks: Tricks of the Trade, pp. 659–686, Springer, 2012.10.1007/978-3-642-35289-8_36Open DOISearch in Google Scholar

[32] C. E. Martin and J. A. Reggia, Fusing swarm intelligence and self-assembly for optimizing echo state networks, Comput. Intell. Neurosci., vol. 2015, p. 9, 2015.Search in Google Scholar

[33] A. A. Ferreira and T. B. Ludermir, Comparing evolutionary methods for reservoir computing pretraining, in Proceedings of the 2011 International Joint Conference on Neural Networks, San Jose, California, USA, pp. 283–290, July 31 - August 5 2011.10.1109/IJCNN.2011.6033233Search in Google Scholar

[34] A. Deihimi and A. Solat, optimised echo state networks using a big bang–big crunch algorithm for distance protection of series-compensated transmission lines, Int. J. Elec. Power., vol. 54, pp. 408–424, 2014.10.1016/j.ijepes.2013.07.024Open DOISearch in Google Scholar

[35] A. A. Ferreira, T. B. Ludermir, and R. R. De Aquino, An approach to reservoir computing design and training, Expert. Syst. Appl., vol. 40, no. 10, pp. 4172–4182, 2013.Search in Google Scholar

[36] D. Liu, J. Wang, and H. Wang, Short-term wind speed forecasting based on spectral clustering and optimised echo state networks, Renew. Energ., vol. 78, pp. 599–608, 2015.10.1016/j.renene.2015.01.022Search in Google Scholar

[37] J. L. Gross and J. Yellen, Handbook of graph theory. CRC press, 2004.10.1201/9780203490204Search in Google Scholar

[38] R. Tarjan, Depth-first search and linear graph algorithms, SIAM J. Comput., vol. 1, no. 2, pp. 146–160, 1972.10.1137/0201010Open DOISearch in Google Scholar

[39] V. Petridis, S. Kazarlis, and A. Bakirtzis, Varying fitness functions in genetic algorithm constrained optimization: the cutting stock and unit commitment problems, IEEE Trans. Syst., Man, Cybern., Part B: Cybern., vol. 28, no. 5, pp. 629–640, 1998.Search in Google Scholar

[40] A. E. Smith and D. M. Tate, Genetic optimization using a penalty function, in Proceedings of the 5th international conference on genetic algorithms, pp. 499–505, Morgan Kaufmann Publishers Inc., 1993.Search in Google Scholar

[41] K. Langfield-Smith and A. Wirth, Measuring differences between cognitive maps, J. Oper. Res. Soc., pp. 1135–1150, 1992.10.1057/jors.1992.180Open DOISearch in Google Scholar

[42] Y.-C. Chuang, C.-T. Chen, and C. Hwang, A simple and efficient real-coded genetic algorithm for constrained optimization, Appl. Soft. Comput., vol. 38, pp. 87–105, 2016.10.1016/j.asoc.2015.09.036Open DOISearch in Google Scholar

[43] J. Lane, A. Engelbrecht, and J. Gain, Particle swarm optimization with spatially meaningful neighbours, in Swarm Intelligence Symposium, 2008. SIS 2008. IEEE, pp. 1–8, IEEE, 2008.10.1109/SIS.2008.4668281Search in Google Scholar

[44] R. C. Eberhart and Y. Shi, Comparing inertia weights and constriction factors in particle swarm optimization, in Proceedings of the 2000 Congress on Evolutionary Computation, vol. 1, pp. 84–88, IEEE, 2000.Search in Google Scholar

[45] S. N. Grösser and M. Schaffernicht, Mental models of dynamic systems: taking stock and looking ahead, Syst. Dynam. Rev., vol. 28, no. 1, pp. 46–68, 2012.10.1002/sdr.476Search in Google Scholar

[46] E. M. Aylward, P. A. Parrilo, and J.-J. E. Slotine, Stability and robustness analysis of nonlinear systems via contraction metrics and sos programming, Automatica, vol. 44, no. 8, pp. 2163–2170, 2008.10.1016/j.automatica.2007.12.012Open DOISearch in Google Scholar

[47] M. Rafferty, Butterflies and buffers, in Proceedings of the 27th International Conference of the System Dynamics Society, Albuquerque, Mexico, USA, July 26-30 2009.Search in Google Scholar

[48] E. Theodorsson-Norheim, Friedman and quade tests: Basic computer program to perform nonparametric two-way analysis of variance and multiple comparisons on ranks of several related samples, Comput. Biol. Med., vol. 17, no. 2, pp. 85–99, 1987.10.1016/0010-4825(87)90003-5Open DOISearch in Google Scholar

[49] M. R. Stoline, The status of multiple comparisons: simultaneous estimation of all pairwise comparisons in one-way anova designs, Am. Stat., vol. 35, no. 3, pp. 134–141, 1981.10.1080/00031305.1981.10479331Search in Google Scholar

[50] K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Trans. Evol. Comput., vol. 6, no. 2, pp. 182–197, 2002.10.1109/4235.996017Open DOISearch in Google Scholar

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