The performance of the two classifiers, namely, WVIMDC and WVINN has been compared for identifying defects on plywood.
The results show that the efficiency of the WVINN is higher than that of the WVIMDC.
This shows that the neural network behaves like a non-linear machine by having more than one layer of weights, and can be trained to learn the non-linear discriminating functions between the different classes.
