Potential of Particle Swarm Optimization and Genetic Algorithms for FIR Filter Design

Boudjelaba, Kamal; Ros, Frédéric; Chikouche, Djamel

doi:10.1007/s00034-014-9800-y

Cited by 27 publications

(13 citation statements)

References 28 publications

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“…Different versions of PSOs and GAs were compared for FIR filter design in Ref. 25, it is pointed out that self-tuning strategies are very important for the improvement of their efficiency. Harmony Search (HS) algorithm 26 is applied for the identification of IIR filters, and excellent results are obtained for the global optimization and rapid convergence.…”

Section: Evolutionary Identification Methodsmentioning

confidence: 99%

“…25 The structure is evolved by GA and is evaluated by a fitness function. Along with structure evolution, the fitness of structures decrease until the algorithm reaches a maximum generation or the fitness satisfies given conditions.…”

Section: Overview Of the Algorithmmentioning

confidence: 99%

“…For example, a 20-instruction-length chromosome crosses with a 15-instruction-length chromosome with a length range of [5,25] in Fig. 3.…”

Section: Crossover Stagementioning

confidence: 99%

See 2 more Smart Citations

Structure Evolution Based Optimization Algorithm for Low Pass IIR Digital Filter Design

Chen¹,

Liu²,

Yang³

et al. 2017

IJCIS

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Digital filters are generally designed by identifying the transfer functions. Most researches are focused on the goal of approaching the desired frequency response, and take less additional consideration of structure characteristics which can greatly affect the performance of the digital filter. This paper proposes a structure-evolution based optimization algorithm (SEOA) which allows the integrated consideration of structure issues and frequency response specifications in design stage. The method generates digital filter structures by a structurally automatic-generation algorithm (SAGA) which can randomly generate and effectively represent digital structures. The structures, seen as chromosomes, are evolved over genetic algorithm (GA) for the search of the optimal solution in structure space. They are evaluated according to the mean squared error (MSE) between the designed and the desired frequency responses. Simulation results validate that the algorithm designs diversified structures of digital filters and they meet target frequency specifications and structure constraints tightly. It is a promising way for optimized and automated design of digital filters.

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Section: Evolutionary Identification Methodsmentioning

confidence: 99%

Section: Overview Of the Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

Structure Evolution Based Optimization Algorithm for Low Pass IIR Digital Filter Design

Chen¹,

Liu²,

Yang³

et al. 2017

IJCIS

View full text Add to dashboard Cite

show abstract

“…There are many researchers focusing on the genetic algorithm to obtain the inverse kinematics of the robot [6][7][8]. Kamal and Djamel [6] researched particle swarm optimization (PSO) and genetic algorithms (GA) for finite impulse response (FIR) filter design.…”

Section: Introductionmentioning

confidence: 99%

A Precise Positioning Method for a Puncture Robot Based on a PSO-Optimized BP Neural Network Algorithm

et al. 2017

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The problem of inverse kinematics is fundamental in robot control. Many traditional inverse kinematics solutions, such as geometry, iteration, and algebraic methods, are inadequate in high-speed solutions and accurate positioning. In recent years, the problem of robot inverse kinematics based on neural networks has received extensive attention, but its precision control is convenient and needs to be improved. This paper studies a particle swarm optimization (PSO) back propagation (BP) neural network algorithm to solve the inverse kinematics problem of a UR3 robot based on six degrees of freedom, overcoming some disadvantages of BP neural networks. The BP neural network improves the convergence precision, convergence speed, and generalization ability. The results show that the position error is solved by the research method with respect to the UR3 robot inverse kinematics with the joint angle less than 0.1 degrees and the output end tool less than 0.1 mm, achieving the required positioning for medical puncture surgery, which demands precise positioning of the robot to less than 1 mm. Aiming at the precise application of the puncturing robot, the preliminary experiment has been conducted and the preliminary results have been obtained, which lays the foundation for the popularization of the robot in the medical field.

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“…Besides that, Boudjelaba et al mentioned that PSO is less complex and requires less parameter to adjust as this technique has no mutation and crossover operator compared to GA. Moreover, PSO tends to converge faster than GA [40]. Therefore in this study, PSO is chosen as the optimization tool and the improver to converge ELM.…”

Section: Literature Reviewmentioning

confidence: 99%

Extreme Learning Machine and Particle Swarm Optimization in optimizing CNC turning operation

Janahiraman¹,

Ahmad

Nordin³

2018

IOP Conf. Ser.: Mater. Sci. Eng.

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Abstract. The CNC machine is controlled by manipulating cutting parameters that could directly influence the process performance. Many optimization methods has been applied to obtain the optimal cutting parameters for the desired performance function. Nonetheless, the industry still uses the traditional technique to obtain those values. Lack of knowledge on optimization techniques is the main reason for this issue to be prolonged. Therefore, the simple yet easy to implement, Optimal Cutting Parameters Selection System is introduced to help the manufacturer to easily understand and determine the best optimal parameters for their turning operation. This new system consists of two stages which are modelling and optimization. In modelling of inputoutput and in-process parameters, the hybrid of Extreme Learning Machine and Particle Swarm Optimization is applied. This modelling technique tend to converge faster than other artificial intelligent technique and give accurate result. For the optimization stage, again the Particle Swarm Optimization is used to get the optimal cutting parameters based on the performance function preferred by the manufacturer. Overall, the system can reduce the gap between academic world and the industry by introducing a simple yet easy to implement optimization technique. This novel optimization technique can give accurate result besides being the fastest technique.

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Potential of Particle Swarm Optimization and Genetic Algorithms for FIR Filter Design

Cited by 27 publications

References 28 publications

Structure Evolution Based Optimization Algorithm for Low Pass IIR Digital Filter Design

Structure Evolution Based Optimization Algorithm for Low Pass IIR Digital Filter Design

A Precise Positioning Method for a Puncture Robot Based on a PSO-Optimized BP Neural Network Algorithm

Extreme Learning Machine and Particle Swarm Optimization in optimizing CNC turning operation

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