Identification of parameters of digital IIR filters using teaching-learning optimization algorithm and statistical inference comparison with Particle Swarm Optimization algorithms

This paper describes a methodology for determining the parameters of a digital IIR filter using a recently developed algorithm Teaching-Learning-Based Optimization (TLBO). The results have been compared with those obtained using two types of Particle Swarm Optimization (PSO) algorithms. For stability of the digital IIR filters, a constraint handling method has been used with all these algorithms applied to identify different systems in noisy environment. The statistical analysis of the simulation results and comparison of mean square error (MSE) shows that TLBO algorithm performs better than PSO algorithms.