Performance Analysis of Transfer function Based Active Noise Cancellation Method Using Evolutionary Algorithm

Due to the exponential increase of noise pollution, the demand for noise controlling system is also increases. Basically two types of techniques are used for noise cancellation active and passive. But passive techniques are inactive for low frequency noise, hence there is an increasing demand of research and developmental work on active noise cancellation techniques. In this paper we introduce a new method in the active noise cancellation system. This new method is the transfer function based method which used Genetic and Particle swarm optimization (PSO) algorithm for noise cancellation. This method is very simple and efficient for low frequency noise cancellation. Here we analysis the performance of this method in the presence of white Gaussian noise and compare the results of Particle swarm optimization (PSO) and Genetic algorithm. Both algorithms are suitable for different environment, so we observe their performance in different fields. In this paper a comparative study of Genetic and Particle swarm optimization (PSO) is described with proper results. It will go in depth what exactly transfer function method, how it work and advantages over neural network based method.