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首页> 外文期刊>WSEAS Transactions on Signal Processing >Adaptive System Identification By Using Artificial Bee Colony Algorithm
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Adaptive System Identification By Using Artificial Bee Colony Algorithm

机译：采用人工蜜蜂菌落算法的自适应系统识别

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摘要

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.
机译：自适应有限脉冲响应（FIR）滤波器的理论和设计是在实践中发育的并且广泛应用于误差表面的简单分析描述和固有稳定行为。然而，对自适应无限脉冲响应（IIR）滤波器的研究不像自适应型冷滤网一样常见。原因在于，在自适应IIR过滤器的设计中存在两个主要缺点：在某些应用中，在某些应用中可能无法确保适应过程中的稳定性，并且由于其多模态误差表面结构，并不总是保证最佳设计的收敛。为了克服这些困难，基于全局优化的方法用于自适应IIR滤波器设计。最近提出的基于群体的全局优化算法之一是人造蜜蜂殖民地（ABC）算法，用于模拟蜜蜂群的智能觅食行为。在这项工作中，描述了一种基于人造群菌落算法的新方法，并应用于自适应IIR滤波器的设计，其性能与差分演进（DE）和粒子群优化（PSO）算法进行了比较。

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