Adaptive System Identification By Using Artificial Bee Colony Algorithm

Çetinkaya M. B.

WSEAS TRANSACTIONS ON SIGNAL PROCESSING, vol.13, pp.121-134, 2017 (Peer-Reviewed Journal)

  • Publication Type: Article / Article
  • Volume: 13
  • Publication Date: 2017
  • Journal Indexes: EBSCO Education Source
  • Page Numbers: pp.121-134
  • Erciyes University Affiliated: Yes


The theory and design of adaptive finite impulse response (FIR) filters are well developed and widely applied in practice due to their simple analytic description of error surfaces and intrinsic stable behavior. However, the studies on adaptive infinite impulse response (IIR) filters are not as common as adaptive FIR filters. The reason is that there are two main drawbacks in the design of adaptive IIR filters: stability during the adaptation process may not be ensured in some applications and the convergence to the optimal design is not always guaranteed because of their multi-modal error surface structures. In order to overcome these difficulties, global optimization based approaches are used in adaptive IIR filter design. One of the most recently proposed swarm intelligence based global optimization algorithms is the artificial bee colony (ABC) algorithm which simulates the intelligent foraging behavior of honeybee swarms. In this work, a novel approach based on artificial bee colony algorithm is described and applied to the design of adaptive IIR filters and its performance is compared to that of differential evolution (DE) and particle swarm optimization (PSO) algorithms.