Calculating the impact factor of neural networks on optimization algorithm for sensor selection

Abstract

Intelligent sensor selection for monitoring operations is one of the serious subjects to reduce information processing time and increase information fusion accuracy. This paper attempts to design an intelligent sensor selection service by using optimization algorithm and neural networks. This service specifies the best group of sensors having the highest recognition rate in each situation. The important part of optimization algorithms is their fitness function. Since in this problem, unlike the problems explained in [1, 2] we can not extract a mathematical fitness function, we use a neural network as an estimator to evaluate the fitness value of each chromosome in genetic algorithm. In this paper, three types of neural network including Multilayer Perceptron (MLP), Radial Basis function (RBF) and ELMAN network are used. Then these three networks are performed within a genetic algorithm and compare their influence on the result of genetic algorithm. We define 500 various scenarios for 6 different sensors in several conditions. Then object recognition rate of each sensor is calculated and used for neural networks training process. After running three different scenarios separately in 10 times, we found that using MLP neural network in genetic algorithm has maximum object recognition rate, 97.6% and minimum time consuming, 22 seconds.