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Digital IIR filters design using differential evolution algorithm with a controllable probabilistic population size.

Wu Zhu1, Jian-an Fang, Yang Tang

  • 1College of Information Science and Technology, Donghua University, Shanghai, China.

Plos One
|July 19, 2012
PubMed
Summary
This summary is machine-generated.

An improved differential evolution (DE) algorithm, controllable probabilistic DE (CPDE), enhances digital infinite-impulse-response (IIR) filter design. CPDE efficiently finds global minima in complex error surfaces, offering a competitive and promising solution.

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Area of Science:

  • Digital Signal Processing
  • Computational Intelligence
  • Filter Design

Background:

  • Digital infinite-impulse-response (IIR) filter design involves synthesizing recursive networks for desired responses.
  • IIR filter error surfaces are often non-linear and multi-modal, complicating global minimum identification.
  • Existing optimization methods may struggle with the complexity of IIR filter design.

Purpose of the Study:

  • To propose an improved differential evolution (DE) algorithm for digital IIR filter design.
  • To address the challenges of non-linear and multi-modal error surfaces in IIR filter optimization.
  • To develop a method that balances convergence speed and computational cost.

Main Methods:

  • Introduction of a controllable probabilistic DE (CPDE) variant with adaptive population sizing.
  • Dynamic adjustment of population size based on fitness diversities to optimize convergence and cost.
  • Analysis of key design aspects including cost function, noise influence, convergence rate, and success percentage.

Main Results:

  • The proposed CPDE algorithm demonstrates viability and competitiveness in digital IIR filter design.
  • Simulation results indicate superior performance compared to six existing state-of-the-art algorithms.
  • CPDE effectively navigates complex error landscapes to find optimal filter parameters.

Conclusions:

  • The CPDE algorithm presents a promising and competitive approach for digital IIR filter design.
  • The adaptive population control mechanism enhances optimization efficiency.
  • CPDE offers a robust solution for overcoming challenges in IIR filter synthesis.