Discrete-time signal processing (2nd ed.)
Discrete-time signal processing (2nd ed.)
Swarm intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Programs for Digital Signal Processing
Programs for Digital Signal Processing
Journal of Global Optimization
A computationally efficient evolutionary algorithm for real-parameter optimization
Evolutionary Computation
Digital filter design using multiple pareto fronts
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Spatial Information Based Image Segmentation Using a Modified Particle Swarm Optimization Algorithm
ISDA '06 Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications - Volume 02
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Design of two-dimensional recursive filters by using neural networks
IEEE Transactions on Neural Networks
Real-time deterministic chaos control by means of selected evolutionary techniques
Engineering Applications of Artificial Intelligence
Filter modeling using gravitational search algorithm
Engineering Applications of Artificial Intelligence
Parameter selection of a Particle Swarm Optimisation dynamics by closed loop stability analysis
International Journal of Computing Science and Mathematics
A new Chance-Variance optimization criterion for portfolio selection in uncertain decision systems
Expert Systems with Applications: An International Journal
Computer Methods and Programs in Biomedicine
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The concept of particle swarms, although initially introduced for simulating human social behaviors, has become very popular these days as an efficient means for intelligent search and optimization. The particle swarm optimization (PSO), as it is called now, does not require any gradient information of the function to be optimized, uses only primitive mathematical operators and is conceptually very simple. This paper investigates a novel approach to the designing of two-dimensional zero phase infinite impulse response (IIR) digital filters using the PSO algorithm. The design task is reformulated as a constrained minimization problem and is solved by a modified PSO algorithm. Numerical results are presented. The paper also demonstrates the superiority of the proposed design method by comparing it with two recently published filter design methods and two other state of the art optimization techniques.