Hardware implementation of evolutionary digital filters

Abe, Masahide; Kawamata, Masayuki

doi:10.1109/iscas.2003.1205857

Cited by 2 publications

(1 citation statement)

References 5 publications

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“…But its effectiveness was affected by convergence time. After that, population-based stochastic optimization algorithms have been discussed in various literatures for design and identification of IIR filters which had the capability of faster convergence and global search of solution space in comparison to conventional methods [10][11][12][13]. Application of particle swarm optimization (PSO) and its variants could be found in [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

IIR filter optimization using improved chaotic harmony search algorithm

Shafaati

Mojallali

2018

Automatika

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Due to the fact that the error surface of adaptive infinite impulse response (IIR) systems is generally nonlinear and multimodal, conventional derivative-based techniques fail when used in adaptive Filter design. In this sense, global optimization techniques are required in order to avoid local minima. Harmony search (HS), a musical inspired metaheuristic, is a recently introduced population-based algorithm that has been successfully applied to global optimization problems. In the present paper, adaptive IIR filtering is formulated as a nonlinear optimization problem and then an improved version of HS incorporating chaotic search (CIHS) is introduced to solve the identification problem of three benchmark IIR systems. Furthermore, the performance of the proposed methodology is compared with HS and two well-known metaheuristic algorithms, genetic algorithm (GA) and particle swarm optimization (PSO) and a modified version of PSO called PSOW (Particle Swarm Optimization with weight Factor). The results demonstrate that the proposed method has superior performance over the other above-mentioned algorithms in terms of convergence speed and accuracy.

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Section: Introductionmentioning

confidence: 99%

IIR filter optimization using improved chaotic harmony search algorithm

Shafaati

Mojallali

2018

Automatika

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Design and FPGA Implementation of a Structure of Evolutionary Digital Filters for Hardware Implementation

Abe

Arai

Kawamata

2005 IEEE International Symposium on Circuits and Systems

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Abstract-In this paper, we design and implement an improved hardware-based evolutionary digital filter (EDF) version 2. The EDF is an adaptive digital filter which is controlled by adaptive algorithm based on evolutionary computation. The hardwarebased EDF version 1 consists of two submodules, that is, a filtering and fitness calculation (FFC) module and a reproduction and selection (RS) module. The FFC module has high computational ability to calculate the output and the fitness value since its submodules run in parallel. However, hardware size of the FFC module is large, and many machine cycles are needed. Thus, in the hardware-based EDF version 2, we combine the two modules to reduce its hardware size and machine cycles. A synthesis result on the FPGA shows the clock frequency is 65.5MHz and the maximum sampling rate of the hardware-based EDF version 2 is 4,948.1Hz. Moreover, the hardware-based EDF version 2 is 15.7 times faster than the hardware-based EDF version 1.

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Hardware implementation of evolutionary digital filters

Cited by 2 publications

References 5 publications

IIR filter optimization using improved chaotic harmony search algorithm

IIR filter optimization using improved chaotic harmony search algorithm

Design and FPGA Implementation of a Structure of Evolutionary Digital Filters for Hardware Implementation

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