Analysis of adaptive IIR filter design based on quantum-behaved particle swarm optimization

Adaptive infinite impulse response (IIR) filters have a wide range of applications such as channel equation, echo canceling and system identification. As the error surface of IIR filters is usually multi-modal, it is necessary to use global optimization techniques to avoid local minima. In this paper, we applied our previously proposed global optimization algorithm, called Quantum-behaved particle swarm optimization (QPSO), to design IIR filters. The quantum behaving in physics and particle swarm optimization had combined to form the new method. The method has some typical characteristic, such as fast convergence rate, global convergence ability, simple coding and easily programming etc, which is proved by simulation experiments at last.