Journal of Mathematical Sciences: Advances and Applications[1] P. S. Addison and D. J. Low, order and chaos in the dynamics of
vehicle platoons, Traffic Engineering + Control, July/August, (1996),
456-459.
[2] A. M. Albano, A. Passamante, T. Hediger and M. E. Farrell, Using
neural nets to look for chaos, Physica D 58 (1992), 1-9.
[3] K. T. Alligood, T. D. Sauer and J. A. Yorke, Chaos: An
Introduction to Dynamical Systems, Springer-Verlag, Inc., New York,
(1997).
[4] L. A. Aquirre and S. A. Billings, Validating identified nonlinear
models with chaotic dynamics, International Journal of Bifurcation and
Chaos in Applied Sciences and Engineering 4(1) (1994), 109-125.
[5] R. Bakker, J. C. Schouten, F. Takens and C. M. van den Bleek,
Neural network model to control an experimental chaotic pendulum,
Physical Review E 54A (1996), 3545-3552.
[6] G. Deco and B. Schurmann, Neural learning of chaotic system
behavior, IEICE Transactions, Fundamentals E77-A(11) (1994),
1840-1845.
[7] H. Demuth and M. Beale, Neural Network Toolbox for Use with
MATLAB, User’s Guide, Version 4, The Math Works, Inc., Natick, MA,
(2004).
[8] J. E. Disbro and M. Frame, Traffic flow theory and chaotic
behavior, Transportation Research Record 1225 (1989), 109-115.
[9] J. D. Farmer and J. J. Sidorowich, Predicting chaotic time series,
Phys. Rev. Letters 59 (1987), 62-65.
[10] H. Fu, J. Xu and L. Xu, Traffic chaos and its prediction based on
a nonlinear car-following model, J. Control Theory Appl. 3(3) (2005),
302-307.
[11] D. C. Gazis, R. Herman and R. W. Rothery, Nonlinear
follow-the-leader models of traffic flow, Operational Research 9(4)
(1961), 545-567.
[12] P. Grassberger and I. Proccacia, Characterization of strange
attractors, Phys. Rev. Letters 50 (1983), 346-349.
[13] M. T. Hagan and M. Menhaj, Training feedforward networks with the
Marquardt algorithm, IEEE Transactions on Neural Networks 5(6) (1994),
989-993.
[14] D. D. Hebb, The Organization Behavior, John Wiley, New York,
(1949).
[15] J. J. Hopfield, Neural networks and physical systems with
emergent collective computational abilities, Proc. Nat. Acad. Sci.,
USA 79(8) (1982), 2554-2558.
[16] J. J. Hopfield, D. I. Feinstein and R. G. Palmers, Unlearning has
a stabilizing effect in collective memories, Nature 304 (1983),
158-159.
[17] K. Levenberg, A method for the solution of certain problems in
least squares, Quart. Appl. Math. 2 (1944), 164-168.
[18] D. J. C. MacKay, Bayesian interpolation, Neural Computation 4(3)
(1992), 415-447.
[19] D. Marquardt, An algorithm for least squares estimation of
nonlinear parameters, SIAM J. Appl. Math. 11 (1963), 431-441.
[20] W. S. McCulloch and W. Pitts, A logical calculus of ideas
immanent in nervous activity, Bull. Math. Biophys. 5 (1943), 115-133.
[21] W. Mendenhall, R. L. Scheaffer and D. D. Wackerly, Mathematical
Statistics with Applications, Third Edition, Duxbury Press, Boston,
(1986).
[22] F. C. Moon, Chaotic and Fractal Dynamics: An Introduction for
Applied Scientists and Engineer, John-Wiley and Sons, New York,
(1992).
[23] J. C. Principe, A. Rathie and J. M. Kuo, Prediction of chaotic
time series with neural networks and the issue of dynamic modelling,
International Journal of Bifurcation and Chaos in Applied Sciences and
Engineering 2 (1992), 989-996.
[24] F. Rosenblatt, The perception: a probabilistic model for
information storage and organization in the brain, Psychological
Review 65(6) (1958), 386-408.
[25] D. E. Rumelhart and J. L. McClelland, Parallel Distributed
Processing: Explorations in the Microstructure of Cognition, Volume 1
(Foundations), The MIT Press, Cambridge, MA, (1986).
[26] F. Takens, Detecting strange attractors in turbulence, Lecture
Notes in Mathematics 898 (1981), 366-381.
