Using a Backpropagation Neural Network in WSN for NOx Detection

Authors

  • Mohammed Hussein Ali , Yusuf Erkan Yenice

Abstract

This study is aim to design and simulate a backpropagation neural network in
wireless sensor network (WSN) in order to process incoming data from number of
sensors and training the network with using a training function to optimize the result and
make a proper decision according to the purpose of design.
The backpropagation neural network considers as one of the most powerful artificial
neural networks using an optimize method to upgrade the biases and weights on the
‘hidden and output layers.
Particle Swarm Optimization (PSO) had been used in this paper as a training
function to optimize the result in neural network. and introduce the best result with
mean square error (MSE) equal to zero.
The system will be simulated and design using MATLAB software package, and the
test of processes given a results matching to desired results of output with mean
square error equal to zero with 46 iterations that improve the previous work done in
this field.

Published

2020-11-01

Issue

Section

Articles