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[4] K. S. Narendra and K. Parthasarathy, “Identification and

Control of Dynamical Systems Using Neural Networks,”

IEEE Transactions on Neural Networks, Vol. 1, No. 1,

1990, pp. 4-27. doi:10.1109/72.80202

[5] L. Chen and K. S. Narendra, “Nonlinear Adaptive Con-

trol Using Neural Networks and Multiple Models,” Pro-

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[6] R. Zhan and J. Wan “Neural Network-Aided Adaptive

Unscented Kalman Filter for Nonlinear State Estimation,”

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[7] A. S. Poznyak, W. Yu, E. N. Sanchez and J. P. Perez,

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Figure 4. Model output, nonlinear dynamical sy stem output

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5. Conclusions and Future Work [10] Xiang Li, Z. Q. Chen and Z. Z. Yuan, “Simple Recurrent

Neural Network-Based Adaptive Predictive Control for

Nonilnear Systems,” Asian Journal of Control, Vol. 4,

No. 2, June 2002, pp. 231-239.

We have presented a new neural network architecture

called based on the adaptation of the shape of the sig-

moid weight of the hidden layer neurons and have intro-

duced its corresponding dynamic back propagation

learning algorithm. This architecture is applied in both

identification and adaptive control of nonlinear dynami-

cal systems and gives better results than the standard

DRNN For the future work, it is suggested that this ar-

chitecture will be extended to be used in multivariable

nonlinear system identification and adaptive control as

well as other practical neural networks applications such

as pattern recognition and time series prediction

[11] N. Kumar , V. Panwar, N. Sukavanam, S. P. Sharma and

J. H. Borm, “Neural Network-Based Nonlinear Tracking

Control of Kinematically Redundant Robot Manipula-

tors,” Mathematical and Computer Modelling, Vol. 53,

No. 9-10, 2011, pp. 1889-1901.

doi:10.1016/j.mcm.2011.01.014

[12] J. Pedro and O. Dahunsi, “Neural Network Based Feed-

back Linearization Control of a Servo-Hydraulic Vehicle

Suspension System,” International Journal of Applied

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[13] A. Thammano and P. Ruxpakawong, “Nonlinear Dy-

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