System Identification Technique and Neural Networks for Material Lifetime Assessment Application - Complex System Modelling and Control Through Intelligent Soft Computations - page 781

Fig. 20 S-N curves obtained
by the NN-NARX model and
the experimental data for
R = − 1: ʸ =90 ° of
E-glass/polyester

Fig. 21 S-N curves obtained
by the NN-NARX model and
the experimental data for
R = 10: ʸ =30 ° of

E-glass/polyester

Fig. 22 S-N curves obtained
by the NN-NARX model and
the experimental data for
R = of

E-glass fabrics/epoxy

− 2: ʸ =90 °

for stress ratios R = , respectively. Note that the NN

simulation results were selected and presented because the results respectively
corresponded to the lowest and the highest of R 2

− 2: ʸ =90 ° and R = 0.5: ʸ =90 °

values which represent the

“ goodness ” of the NN-NARX model in modeling fatigue lives for the problem

considered.