Hybrid Indoor-Based WLAN-WSN Localization Scheme for Improving Accuracy Based on Artificial Neural Network

Pseudocode 1

) // initialize the ANN model

  (a) ; // initialize the learning rate

  (b) ; // initialize the momentum

  (c) for each weight   

      = rand (); // initialize the weight with small random number

() while (number of iterations < MAX and distance error > ) // training loop

// MAX and are constant threshold values

for each pattern in the training set

  for each layer in the ANN

    for each node in the layer

     (a) Calculate weighted sum of inputs to (Equation (1));

     (b) Add the bias value to the calculated sum (Equation (2));

     (c) Calculate the activation function for (Equation (3));

  Back propagate error through output layer (Equation (4));

  Back propagate error through hidden layer (Equation (5));

  for each weight   

    Update (Equation (6));

  Calculate distance error (Equation (11)); // Equation (12) for testing data

() Repeat Step () for the testing dataset