Abstract:
Digital filtering is one of the main fundamental aspect of digital signal processing (DSP). Finite Impulse Response (FIR) digital filter design involves multi parameter optimization, on which the existing optimization algorithm may does not work efficiently. Particle Swarm Optimization (PSO) algorithm is a bio-inspired optimization algorithm which has been empirically demonstrated to perform well on many optimization problems. It is widely used to find the global optimum solution in a complex search space.
This paper presents the design of linear phase low pass FIR filter using particle swarm optimization (PSO) algorithm and discuss the influence of changing the PSO algorithm parameters such as the inertia weight (w), cognitive (c1) and social (c2) on the FIR filter design problem. Also the linear phase low pass FIR filter has been designed using the conventional genetic algorithm (GA) and a comparison has been made.
The simulation results show that PSO algorithm is better than the conventional GA with more rapidly convergence speed and better performance of the designed filter.
The FIR filter design using PSO algorithm is simulated using MATLAB programming language version 7.
