IIR Filter Design Using Multiobjective Artificial Bee Colony Algorithm

Raju, Rija; Kwan, Hon Keung

doi:10.1109/ccece.2018.8447836

Cited by 5 publications

(4 citation statements)

References 8 publications

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“…Such designs results into ripple and error in the output signal which affects overall signal quality. Considering meta heuristic based design parameter optimization methods [22][23][24][25][26][27][28][29][30][31], though these methods play vital role towards square error or ripples suppression within passband and stopband, however at the cost of high computational cost, delay etc. It might even increase memory and area consumption making them unsuitable for VLSI or FPGA implementation.…”

Section: Related Workmentioning

confidence: 99%

“…To achieve symmetric boost and cut responses dynamically (conditioned at ), the following is applied [36][37][38]. (26) In above derived model the linear dependency in reference to the gain parameter is lost and therefore we considered only LIG form to achieve automatic parametric tuning and dynamic equalisation over non-linear input signal. Though, in both NLIG and LIG form shelving or peaking filters can be applied to obtain and update filter parameters dynamically.…”

Section: Second Order Peaking Iir Filtermentioning

confidence: 99%

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Parametric Continuously Programmable Infinite Impulse Response Filter for Non-Linear Real-Time DSP Applications

Elavarasi¹,

Manjunath²

2020

IJETAE

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Considering the exponentially rise in non-linear signal filtering purposes, in this paper a novel parametric continuously programmable infinite impulse response (CPIIR) filter has been developed. Realizing the key upsurging significance of non-linear audio signal filtering, our proposed CPIIR filter model has been realized over parametric equalization system, which can have one or multiple IIR filters in cascade-design to perform continuous filtering. Structurally, our proposed model represents a PE solution containing shelving and second-order peaking CPIIR filters whose design parameters are optimized dynamically by reducing cost-function iteratively. Unlike classical approaches where merely filter coefficients are changed manually to cope up with non-linear signal filtering, our proposed parametric IIR filter employs sum-of-square error (SSE) as the costfunction to update filter design parameters like gain parameter, frequency and bandwidth. Noticeably, the use of SSE as cost-function intends to reduce error (i.e., difference between the target signal and the system frequency response) to optimize global gain parameter that eventually help updating other design parameter adaptively over different frequency-bins or windows. This process retains system response near 0-dB line and thus maintains optimal filtering performance over swiftly varying signal response. Unlike major at hand meta-heuristic based search approaches for parameter tuning, our proposed model applies simple grid search concept that avoided significantly large computation and hardware requirements. MATLAB based simulation with Gaussian white noise embedded non-linear signals reveals that the proposed parametric CPIIR model can achieve optimal filtering performance and suitability towards hardware implementation.

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Section: Related Workmentioning

confidence: 99%

Section: Second Order Peaking Iir Filtermentioning

confidence: 99%

Parametric Continuously Programmable Infinite Impulse Response Filter for Non-Linear Real-Time DSP Applications

Elavarasi¹,

Manjunath²

2020

IJETAE

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“…Pelusi, et al [5] have implemented a fuzzy gravitational based search algorithm in order to design optimal IIR filters. Raju & Keung Kwan [6] designed an IIR filter using a multi-objective artificial bee colony (ABC) algorithm.…”

Section: Introductionmentioning

confidence: 99%

On Minimal Second-order IIR Bandpass Filters with Constrained Poles and Zeros

Joelianto

2021

J. Eng. Technol. Sci.

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In this paper, several forms of infinite impulse response (IIR) bandpass filters with constrained poles and zeros are presented and compared. The comparison includes the filter structure, the frequency ranges and a number of controlled parameters that affect computational efforts. Using the relationship between bandpass and notch filters, the two presented filters were originally developed for notch filters. This paper also proposes a second-order IIR bandpass filter structure that constrains poles and zeros and can be used as a minimal parameter adaptive digital second-order filter. The proposed filter has a wider frequency range and more flexibility in the range values of the adaptation parameters.

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“…In recent years, evolutionary algorithms [24] have emerged as a useful class of optimisation algorithms that find increasing applications in diverse fields. For digital filter design: artificial bee colony (ABC) algorithm has been applied to design linear phase FIR differentiators [25], non‐linear phase FIR filters [26], sparse FIR filters [27], and IIR filters [28]; cuckoo search algorithm has been applied to design linear phase FIR filters [29], non‐linear phase FIR filters [30], sparse FIR filters [31, 32], two‐dimensional sparse FIR filters [33], and IIR filters [34]; teaching–learning‐based optimisation has been applied to design linear phase FIR Hilbert transformers [35], non‐linear phase FIR filters [36], IIR filters [37], two‐dimensional linear phase FIR digital filters [38], and two‐dimensional non‐linear phase FIR filters [39]; harmony search (HS) algorithm has been applied to design non‐linear phase FIR filters [40], and all‐pass equalisers [41, 42]; and the interactive self‐learning algorithm has been applied to design non‐linear phase FIR filters [43].…”

Section: Introductionmentioning

confidence: 99%

Design of p ‐norm linear phase FIR differentiators using adaptive modification rate artificial bee colony algorithm

Kwan

Raju

2020

IET signal process.

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In this paper, an adaptive modification rate artificial bee colony (AMR‐ABC) algorithm is proposed by incorporating a novel adaptive modification rate to adaptively balance exploration and exploitation to determine which parameters (or the number of parameters) to be updated in a solution during each iteration. The performance of the AMR‐ABC algorithm is compared to those the standard ABC algorithm and its two variants, and the Parks–McClellan algorithm for designing Type 3 (orders: 14, 26, and 38) and Type 4 (orders: 13, 25, and 37) linear phase FIR differentiators to evaluate their design capabilities. Design results have shown that the proposed AMR‐ABC algorithm (i) outperforms four other design algorithms with the lowest p‐norm error in each of the six differentiator designs and (ii) is robust such that the same p‐norm error solution with an equiripple amplitude response in each of the six differentiator designs can be obtained by repeating a design with a different population of randomly generated initial solutions. The filter coefficients of six linear phase FIR differentiator designs are given as benchmarks to compare the p‐norm error performance of the AMR‐ABC algorithm to other algorithms. The AMR‐ABC algorithm is attractive to be used for optimisation in this and other design problems.

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IIR Filter Design Using Multiobjective Artificial Bee Colony Algorithm

Cited by 5 publications

References 8 publications

Parametric Continuously Programmable Infinite Impulse Response Filter for Non-Linear Real-Time DSP Applications

Parametric Continuously Programmable Infinite Impulse Response Filter for Non-Linear Real-Time DSP Applications

On Minimal Second-order IIR Bandpass Filters with Constrained Poles and Zeros

Design of p ‐norm linear phase FIR differentiators using adaptive modification rate artificial bee colony algorithm

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