Design of canonical signed digit IIR fiilters using genetic algorithm

Liang, Li; Ahmadi, M.; Sid-Ahmed, M.A.; Wallus, K.

doi:10.1109/acssc.2003.1292339

Cited by 5 publications

(4 citation statements)

References 10 publications

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“…This indicates that the system is prone to feedback and instability. The Digital filters have various stages in their design [14,15] as shown in Figure 1.…”

Section: Iir Filter Design Issuesmentioning

confidence: 99%

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Genetic Algorithm for the Design of Optimal IIR Digital Filters

Singh¹,

Arya²

2012

JSIP

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This paper presents the design of Optimal Infinite-Impulse Response (IIR) digital filters using Genetic Algorithm (GA). IIR filter is essentially a digital filter with Recursive responses. Since the error surface of digital IIR filters is generally nonlinear and multimodal, global optimization techniques are required in order to avoid local minima. This paper presents heuristic way for the designing IIR filters. GA is a powerful global optimization algorithm introduced in combinatorial optimization problems. The paper finds the optimum Coefficients of IIR digital filter through GA. Design of Lowpass and High pass IIR digital filter is proposed to provide estimate of transition band. It is found that the calculated values are more optimal than fda tool available for the design of filter in MATLAB. The simulation result of the employed examples shows an improvement on transition band and mean-square-error (MSE). The position of pole-zero is also presented to describe stability and results are compared with Simulated Annealing (SA) method

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“…This indicates that the system is prone to feedback and instability. The Digital filters have various stages in their design [14,15] as shown in Figure 1.…”

Section: Iir Filter Design Issuesmentioning

confidence: 99%

“…Moreover, it is computationally very expensive. Ahmadi et al (2003) used genetic algorithm to design 1-D IIR filter with canonical-signed-digit coefficients restricted to low-pass filter. Ahmad and Antoniou (2006) explored FIR filters and equalizers through the use of GA. Consequently GAs requires a large amount of computation.…”

Section: Introductionmentioning

confidence: 99%

Genetic Algorithm for the Design of Optimal IIR Digital Filters

Singh¹,

Arya²

2012

JSIP

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“…The most common method [9], [11] is to simply reject any filter that is not stable or give a fitness value of zero, which is virtually the same thing. This method has the same problems associated with it as the CSD constraints of the previous section.…”

Section: Recursive Filtersmentioning

confidence: 99%

Design of 2D FIR and IIR Digital Filters with Canonical Signed Digit Coefficients Using Singular Value Decomposition and Genetic Algorithms

Williams

Ahmadi

Miller

2007

Circuits Syst Signal Process

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In this paper a design approach for two-dimensional finite impulse and infinite impulse response digital filters with canonic signed digit (CSD) coefficients based on singular value decomposition and genetic algorithms (GAs) is presented. The proposed technique uses a new chromosome coding scheme to alleviate the problem encountered by many GAs during crossover and mutation which destroys the CSD property. Examples are given to demonstrate the usefulness of the proposed technique. A comparative study carried out with some of the existing techniques indicates the high throughput property of the proposed technique.

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“…The genetic algorithm (GA) has obtained large interest in application to digital IIR filter design . In Liang et al, they used GA to design a 1‐D IIR filter with canonical‐signed‐digit coefficients constrained to a low‐pass filter; Ahmad and Antoniou investigated the use of GAs for the design of numerous kinds of digital filters. It was found that GAs require a large amount of computation.…”

Section: Introductionmentioning

confidence: 99%

Optimal design of IIR filters based on least ℓ_p‐norm using a novel meta‐heuristic algorithm

Ghibeche

Saadi

Hafaifa

2018

Int J Numerical Modelling

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In this work, an optimal infinite impulse response (IIR) filter is designed using least P th norm and new meta-heuristic optimization algorithm called Crow Search Algorithm (CSA). The two approaches try to get a resolution to the specified problem but use unlike approaches to perform so. Furthermore, the ℓp-norm is used to design optimum IIR filters. The main motivation of using CSA is its user-friendly optimization tool for both beginner and professional users. This Algorithm provides a good stability between variety and strength which are mainly controlled by the parameter of awareness probability. Some examples of IIR filter design cases are treated, and a comparison is done. It is revealed that the design objective is successfully attained, and the resultant filter parameters are better using the ℓp-norm combined with the proposed CSA meta-heuristic optimization algorithms compared with IIR filter designed using butter synthesis approach in terms of accuracy and robustness. Classical approaches are largely outperformed by CSA for the same filter order.

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Design of canonical signed digit IIR fiilters using genetic algorithm

Cited by 5 publications

References 10 publications

Genetic Algorithm for the Design of Optimal IIR Digital Filters

Genetic Algorithm for the Design of Optimal IIR Digital Filters

Design of 2D FIR and IIR Digital Filters with Canonical Signed Digit Coefficients Using Singular Value Decomposition and Genetic Algorithms

Optimal design of IIR filters based on least ℓ_p‐norm using a novel meta‐heuristic algorithm

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Design of canonical signed digit IIR fiilters using genetic algorithm

Cited by 5 publications

References 10 publications

Genetic Algorithm for the Design of Optimal IIR Digital Filters

Genetic Algorithm for the Design of Optimal IIR Digital Filters

Design of 2D FIR and IIR Digital Filters with Canonical Signed Digit Coefficients Using Singular Value Decomposition and Genetic Algorithms

Optimal design of IIR filters based on least ℓp‐norm using a novel meta‐heuristic algorithm

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Optimal design of IIR filters based on least ℓ_p‐norm using a novel meta‐heuristic algorithm