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Chaos theory and Artificial Neural Network
Transcript of Chaos theory and Artificial Neural Network
Auslander, J., Bhatia, N.P., Seibert, P. : Attractors in dynamical systems, Bol. Soc. Mat. Mex. 9, 55-66.
Grebogi, Ott, Pelikan, Yorke (1984). "Strange attractors that are not chaotic". Physica D 13: 261–268. doi:10.1016/0167-2789(84)90282-3.
Conley, C. : "Isolated Invariant Sets and the Morse Index," C.B.M.S. Regional Lect. 38, A.M.S.
J.E. Flaherty, F.C. Hoppensteadt, Frequency entrainment of a forced van der Pol oscillator, Studies in Appl. Math., 58, 5-15, 1978.
P. C. R. Ã, ―Chaos and hyper chaos in a Hopfield neural network,‖ Neurocomputing, vol.
. 4, no. 1., pp. 3361-3364, 2011.
Ruelle, David (August 2006). "What is...a Strange Attractor?" (PDF). Notices of the American Mathematical Society 53 (7): 764–765. Retrieved 2008-01-16.
Woolley, J. W., Agarwal, P. K., & Baker, J. (2010). Modeling and prediction of chaotic systems with artificial neural networks, (July 2009), 989-1004.
Krishnaiah, J., Kumar, C. S., & Faruqi, M. a. (2006). Modelling and control of chaotic processes through their Bifurcation Diagrams generated with the help of Recurrent Neural Network models: Part 1—simulation studies. Journal of Process Control, 16(1), 53-66. 2005.04.002
Dudul, S. V. (2005). Prediction of a Lorenz chaotic attractor using two-layer perceptron neural network. Applied Soft Computing, 5(4), 333-355. 2004.07.005.
Kaslik, E., & Balint, S. (2009). Complex and chaotic dynamics in a discrete-time-delayed Hopfield neural network with ring architecture. Neural Networks, 22(10), 1411-1418. Elsevier Ltd.2009.03.009
Han, M., & Wang, Y. (2009). Analysis and modeling of multivariate chaotic time series based on neural network. Expert Systems With Applications, 36(2), 1280-1290. Elsevier Ltd. 2007.11.057
W.-zhi Huang, “CHAOS , BIFURCATION AND ROBUSTNESS OF A CLASS OF HOPFIELD NEURAL NETWORKS,” IJBC, vol. 21, no. 3, pp. 885-895, 2011.
Zheng, P., Ã, W. T., & Zhang, J. (2010). Some novel double-scroll chaotic attractors in Hopfield networks. Neurocomputing, 73(10-12), 2280-2285. Elsevier. 2010.02.015
Torres, J., & Cort, M. (2007). Chaotic hopping between attractors in neural networks. Neural Networks, 20, 230-235. 2006.11.005.
Min, X., Jian’an, F., Yang, T., & Zhijie, W. (2010). Dynamic depression control of chaotic neural networks for associative memory. Neurocomputing, 73(4-6), 776-783.
L. Shen and M. Wang, “Adaptive control of chaotic systems based on a single layer neural network,” Physics Letters A, vol. 368, no. 5, pp. 379-382, Aug. 2007.
G. He, L. Chen, and K. Aihara, “Associative memory with a controlled chaotic neural network,” Neurocomputing, vol. 71, no. 13–15, pp. 2794-2805, Aug. 2008.
Huang, Z., Mohamod, S., & Bin, H. (2010). Multiperiodicity analysis and numerical simulation of discrete-time transiently chaotic non-autonomous neural networks with time- varying delays. Communications in Nonlinear Science and Numerical Simulation, 15(5), 1348-1357. Elsevier B.V. 2009.05.060.
Sussillo, D., & Abbott, L. F. (2009). Generating Coherent Patterns of Activity from Chaotic Neural Networks. Neuron, 63(4), 544-557. Elsevier Ltd. 2009.07.018
J. M. Bahi, J. Couchot, C. Guyeux, and M. Salomon, “Neural networks and chaos : Construction , evaluation of chaotic networks , and prediction of chaos with multilayer feedforward networks Neural networks and chaos : Construction , evaluation of chaotic networks , and prediction of chaos with multilayer fe,” vol. 013122, no. May, 2012.
Jaeger, H., and Haas, H. (2004). Harnessing nonlinearity: predicting chaotic systems and saving energy in wireless communication. Science 304, 78–80.
Jaeger, H., and Haas, H. (2004). Harnessing nonlinearity: predicting chaotic