An Improved ANN–BPN to Radial Distribution System Load Flow - Proceedings of All India Seminar on Biomedical Engineering 2012 (AISOBE-2012) - page 187

Fig. 3 Neural network

Fig. 4 Basic BPN
architecture

ANN-BPN Architecture

The most widely used learning algorithm in an ANN, the Back propagation
algorithm is presented in this context. The neural network structure defines its
structure including number of hidden layers, number of hidden nodes, and number
of output nodes (Fig. 3 ). Figure 4 shows the architecture of BPN to have three
layers, namely—input, hidden, and the output layers.
The Multilayered perceptron (MLP) network is trained using one of the
supervised learning algorithms of which is the best. It uses the data to adjust the
network's weights and thresholds so as to minimize the error in its predictions on
the training set. Many different sets of the input and their corresponding output
vectors are considered during the training. To determine the weights between the
input, hidden, and output layers, the training phase is used.
Typically in this study neurons used are the sigmoid activation function defined
by the below equation: