The goal of the ELM is to reduce the length of the learning process for the neural network, usually very long (e.g., if using classical back-propagation algorithms).
The FLANN model can be established by using the Error Back-propagation algorithm.
The network has been trained using the back-propagation algorithm.
The locations of the SMGAs are constrained using the back-propagation method of Kurahashi and Irikura (2010).
The linear combination weights in the network are subsequently adjusted to decrease the error using the back-propagation procedure.
The most popular neural network is the multilayer perceptron (MLP) trained using the error back propagation (BP) algorithm [2].
The network architectures considered are the usual feed forward back propagation trained using the standard error back propagation as well as the cascade correlation training schemes, relatively less used configurations of radial basis function and adaptive neuro-fuzzy inference system.
We used the Back-propagation learning algorithm [ 37] and the values used for all the parameters are reported in Table 7.
To demonstrate our results, we had taken the WBCD database from UCI Machine Learning repository and compared the classification accuracy, sensitivity, specificity, confusion matrix, ROC curves and AUC under ROC curves of GONN with classical model and classical back propagation model.
