Fundamentals of Digital Signal Processing with Cdrom
Fundamentals of Digital Signal Processing with Cdrom
An adaptive neural fuzzy filter and its applications
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
GSA: A Gravitational Search Algorithm
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Parameter estimation of bilinear systems based on an adaptive particle swarm optimization
Engineering Applications of Artificial Intelligence
Filter modeling using gravitational search algorithm
Engineering Applications of Artificial Intelligence
Parallel computation models of particle swarm optimization implemented by multiple threads
Expert Systems with Applications: An International Journal
A two-phase algorithm for product part change utilizing AHP and PSO
Expert Systems with Applications: An International Journal
Intelligent identification and control using improved fuzzy particle swarm optimization
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
ICMB'10 Proceedings of the Second international conference on Medical Biometrics
Chaotic secure communication based on a gravitational search algorithm filter
Engineering Applications of Artificial Intelligence
A chaotic digital secure communication based on a modified gravitational search algorithm filter
Information Sciences: an International Journal
Digital Signal Processing
Hi-index | 12.06 |
This paper presents a particle swarm optimization (PSO) algorithm to solve the parameter estimation problem for nonlinear dynamic rational filters. For the modeling of the nonlinear rational filter, the unknown filter parameters are arranged in the form of a parameter vector which is called a particle in the terminology of PSO. The proposed PSO algorithm applies the velocity updating and position updating formulas to the population composed of many particles such that better particles are generated. Because the PSO algorithm manipulates the parameter vectors directly as real numbers rather than binary strings, implementing the PSO algorithm into the computer programs becomes fairly easy and straightforward. Finally, an illustrative example for the modeling of the nonlinear rational filter is provided to show the validity, as compared with the traditional genetic algorithm, of the proposed method.