Name
Abstract | ||
|---|---|---|
FIR digital filters design involves multi-parameter optimization, on which the existing optimization algorithm doesn't work efficiently. This paper focuses on employing the proposed Quantum-behaved Particle Swarm Optimization (QPSO) to design FIR digital filters. QPSO is a global stochastic searching technique that can find out the global optima of the problem more rapidly than original PSO. After describing the origin and development of QPSO, we present how to use it in FIR digital filters design. It has been demonstrated by experiment results that QPSO outperforms the PSO and Genetic Algorithm (GA) for the problem. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/ICICIC.2006.77 | ICICIC (1) |
Keywords | Field | DocType |
original pso,multi-parameter optimization,fir digital filter,genetic algorithm,existing optimization algorithm,fir digital filters design,experiment result,quantum-behaved particle swarm optimization,proposed quantum-behaved particle swarm,global optimum,global stochastic,stochastic processes,fir filters,quantum computing | Particle swarm optimization,Quantum,Mathematical optimization,Digital filter,Control theory,Computer science,Stochastic process,Quantum computer,Multi-swarm optimization,Finite impulse response,Genetic algorithm | Conference |
ISBN | Citations | PageRank |
0-7695-2616-0 | 13 | 0.90 |
References | Authors | |
3 | 4 | |