FIR Digital Filters Design: Particle Swarm Optimization Utilizing LMS and Minimax Strategies

Optimal Design of a Linear Phase Fir Filter Based on Particle Swarm Optimisation

“…PSO is less vulnerable of getting trapped on local optima contrasting GA. Population in PSO is called as swarm and each individual of that population is called as particle whose global optima are searched through the solution space. The fitness function of particles at different locations is obtained iteratively and best fitness values are kept for further calculations [13,14]. Best value of every particle (p best ) is known along with the group best (g best ).Considering the distance between the present position and the p best and the distance between the present position and the g best , particles change their positions.…”

Design of Linear Phase Low Pass FIR Filter using Particle Swarm Optimization Algorithm

“…(8) specifies the random numbers uniformly distributed from decimal zero to one updated every time they occur. The factor called constriction factor is defined in [17] and is given by the formula in Eq. (10).…”

“…PSO using LMS and minimax strategies [17] is used for LP filter design. But all these algorithms have results that yet need to be optimized due to their inherent problems of control parameter dependence, search stagnation or premature convergence.…”

Low power FIR filter design using modified multi-objective artificial bee colony algorithm

“…Some of these challenges refer to the minimization of pass band ripple (PBR), stop band ripple (SBR), low power consumption and smaller filter order. In this context, a large number of optimization based algorithms have been proposed which aim at meeting a set of predefined objectives (Ababneh and Bataineh, 2008;Ghoshal et al, 2012;Karaboga and Cetinkaya, 2006;Najjarzadeh and Ayatollahi, 2008;Psarakis, 2006;Radecki et al, 2005;Saha et al, 2013aSaha et al, , 2013c. Traditionally FIR filters have been designed based on frequency domain specifications.…”