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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
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