Power-of-two adaptive filters using tabu search

Traferro, S.; Uncini, Aurelio

doi:10.1109/82.847077

Cited by 16 publications

(4 citation statements)

References 14 publications

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“…The optimisation process is handled with the non-dominated sorting genetic algorithm (NSGA-II) in its second version. Evolutionary algorithms are already manipulated for the optimisation of IIR filter stability (Ma and Cowan, 1996) and FIR filters (Traferro et al, 1999;Traferro and Uncini, 2000).…”

Section: Introductionmentioning

confidence: 99%

Multiobjective genetic algorithm-based adaptive wavelet design

Ghribi

Jammoussi

Masmoudi

2016

IJCVR

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Wavelet performances differ from one application to another and from one database to another. In this case, one can try to find out for each application the appropriate wavelet transform which results in better performances and consumes minimal resources once implemented on an FPGA platform. Accordingly, we use a generic lifting wavelet transform with p0 and q parameters. Thus, we train the optimisation process with a multi-objective genetic algorithm for optimising wavelet transform with respect to specific applications. Optimised criteria are related to wavelet regularity and the undertaken application performances. We consider pattern recognition and image compression applications processed respectively on ORL database and a fingerprint database. Compared with DB 4 and DB 9/7, the improvement of the criterion related to the application reaches 17 % for pattern recognition application and preserves, approximately, the same values for still image compression in addition to the minimisation of the hardware cost.

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Section: Introductionmentioning

confidence: 99%

Multiobjective genetic algorithm-based adaptive wavelet design

Ghribi

Jammoussi

Masmoudi

2016

IJCVR

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“…This approach has become very popular and many different algorithms have been proposed. These include the linear programming technique [4,9,12], the stochastic optimization techniques such as simulated annealing [1,2,5], tabu search [15], simulated evolution [14], and other heuristic methods such as the proportional relation-preserve method [21].…”

Section: Introductionmentioning

confidence: 99%

A new hybrid descent method with application to the optimal design of finite precision FIR filters

Yiu

Yan

Teo

et al. 2010

Optimization Methods and Software

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In this paper, the problem of the optimal design of discrete coefficient FIR filters is considered. A novel hybrid descent method, consisting of a simulated annealing algorithm and a gradient-based method, is proposed. The simulated annealing algorithm operates on the space of orthogonal matrices and is used to locate descent points for previously converged local minima. The gradient-based method is derived from converting the discrete problem to a continuous problem via the Stiefel manifold, where convergence can be guaranteed. To demonstrate the effectiveness of the proposed hybrid descent method, several numerical examples show that better discrete filter designs can be sought via this hybrid descent method.

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“…The tabu search (TS) algorithm which is based on the general tenets of intelligent problem solving is a modern heuristic developed particularly for combinatorial optimisation problems. Although it has been proposed for combinatorial problems, it has also produced efficient solutions for numeric problems, too (Glover, 1986;Osman, 1991;Ni Guangzhen and Yan, 1998;Fanni et al, 1999;Traferro and Uncini, 2000;Machado et al, 2001). A basic version of the algorithm employs a neighbourhood search mechanism and uses deterministic transition rules to improve solutions.…”

Section: Introductionmentioning

confidence: 99%

A parallel tabu search algorithm for digital filter design

Kalınlı

Karaboğa

2005

COMPEL

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Purpose -The purpose of the paper is to present a novel design method for the optimal finite word length (FWL) finite impulse response (FIR) filters. Design/methodology/approach -The design method is based on a parallel tabu search (TS) algorithm which uses the crossover operator of the genetic algorithm. Findings -Three design examples have been presented to show that the proposed method can provide a good solution to the design problem of a FWL FIR filter. In order to show the validity of the proposed method, the performance of the suggested method has been compared to those of widely-used other methods. From the comparison results, it was concluded that the proposed method can be efficiently used for the optimal FWL FIR filter design.Research limitations/implications -The number of examples can be increased and also the performance of the proposed method might be compared to other design methods, apart from those presented in this work, developed for the design of optimal FWL FIR filters. Practical implications -The use of this method produces optimal digital FWL FIR filters with low complexity and therefore provides advantages in the terms of speed and cost. Originality/value -The originality is the application of the parallel TS algorithm described by the authors to the FWL FIR filter design. The work presented in the paper is particularly important for the researchers studying on the design methods for FWL FIR filter design and the applications of these type filters.

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Power-of-two adaptive filters using tabu search

Cited by 16 publications

References 14 publications

Multiobjective genetic algorithm-based adaptive wavelet design

Multiobjective genetic algorithm-based adaptive wavelet design

A new hybrid descent method with application to the optimal design of finite precision FIR filters

A parallel tabu search algorithm for digital filter design

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