Digital IIR filter design using particle swarm optimisation

Chen, Sheng; Luk, B.L.

doi:10.1504/ijmic.2010.033208

Cited by 84 publications

(47 citation statements)

References 32 publications

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“…In addition, they both have been applied to function optimization [7,8], PID controller [9][10][11] and digital filter design [12,13]. So, the proposed method is compared mainly with the two algorithms.…”

Section: Performance Testmentioning

confidence: 99%

See 1 more Smart Citation

Seeker Optimization Algorithm for Several Practical Applications

Zhu¹,

Dai²,

Chen³

2014

IJCIS

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Optimization problems can often be simplified to the search for an optimal solution in the feasible search space. Based on the concept of simulating the act of human randomized search, a novel algorithm called seeker optimization algorithm (SOA) for real-parameter optimization is proposed in this paper. In the SOA, after given center point, search direction, search radius, and trust degree, every seeker moves to a new position (a candidate solution) from his current position based on his historical and social experiences. In this process, the update formula is like Y-conditional cloud generator. The algorithm's performance was studied using several typically complex functions. In all cases studied, SOA is superior to continuous genetic algorithm (CGA) greatly in terms of optimization quality, robustness and efficiency. At the same time, SOA greatly outperforms particle swarm optimization (PSO) in convergence speed. However, SOA needs more computation time. Simulations of designing both PID controller and IIR digital filter also show that SOA gets more satisfactory solutions with better evaluation values.

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Section: Performance Testmentioning

confidence: 99%

“…Because there are no analytical methods evidently when IIR digital filters with any frequency responses are considered, optimization methods are used generally [2,12,13]. In this research, SOA is used to optimize design of IIR digital filter in frequency domain directly.…”

Section: Designing Iir Digital Filtermentioning

confidence: 99%

Seeker Optimization Algorithm for Several Practical Applications

Zhu¹,

Dai²,

Chen³

2014

IJCIS

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show abstract

“…But, almost all physical systems are nonlinear to certain extent and recursive in nature and hence it is more convincing to model such systems by using nonlinear models. Hence these are better modeled as Infinite Impulse Response (IIR) models as they can provide better performance than a Finite Impulse Response (FIR) filter with the same number of coefficients [11]. Thus the problem of nonlinear system identification can also be viewed as a problem of adaptive IIR filtering.…”

Section: System Identification Problemmentioning

confidence: 99%

Modified PSO based Adaptive IIR Filter Design for System Identification on FPGA

Gupta¹,

Mehra²

2011

IJCA

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Field programmable gate arrays (FPGAs) are becoming increasingly important implementation platforms for digital circuits. This paper focuses on the implementation of Adaptive Infinite Impulse response (IIR) filter on an FPGA using Modified Particle Swarm Optimization (PSO) Algorithm. The proposed Filter is capable of finding the global optimum solution for system identification problem in less number of iterations. The modified PSO algorithm has been developed and simulated using MATLAB. The result shows the enhanced speed of purposed design in terms of number of iterations it takes to identify the unknown system. The same algorithm has also been realized on various Xilinx FPGA devices and performances have also been analyzed. The area utilization by the proposed design on different FPGA devices has been compared. The results show that proposed filter is consuming very less area in terms of LUTs and Slices to provide enhanced area efficiency.

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“…In addition, if W represents the current location of each particle, then another particle that corresponds to  should be generated to represent particle speed. Fitness function   F W assesses the pros and cons of the current location [5]. Among these variables, W is an m-dimensional variable; therefore,  should be an m-dimensional variable as well so that the particle can adopt the following code structure:…”

Section: Introductionmentioning

confidence: 99%

Online Correction of the Dynamic Errors in a Stored Overpressure Measurement System

Wang

Zhang

2015

TELKOMNIKA

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The problem encountered in sharp shock testing as a result of inadequate bandwidth must be addressed to obtain IntroductionA dynamic testing system is installed underground to test explosion shockwaves. A sensor interface is located above ground. The spectrum of the pressure sensor cannot cover the signal spectrum under the influence of the cliff front of the shockwave. Thus, signal amplitude increases significantly. The areas selected by the dynamic testing system are shocked sharply, and peak overpressure cannot be determined accurately when the sensor approaches resonant frequency. Hence, the amplitude frequency characteristics of a piezoelectric sensor should be compensated. Compensation methods include inverse filtering, the collocation of zero poles, and system identification [1]. In addition, neural network, particle swarm optimization (PSO) algorithm, and other algorithms are adopted to improve compensation precision.Neural networks can easily be trapped in the local minimum regardless of network search speed, and Internet precision is difficult to improve at latter stages of training. Although the PSO algorithm is a holistic optimal algorithm, the initial position of the particle affects the optimization results of this algorithm [2]. On the basis of the aforementioned principles, the present paper proposes an improved PSO algorithm that employs adaptive neural networks to determine the optimal initial value for each particle in the particle swarm within the shortest time. This algorithm eventually yields a holistic optimal value.The dynamic compensator is normally of high order, and the dynamic errors of the sensor are difficult to amend quickly and in real time according to the programming ideas of the traditional displacement summation [3]. The current study establishes a parallel method to develop hardware for dynamic compensation filtering according to the concepts of the distributed algorithm. This method converts the index of optimal dynamic compensation filtering as obtained with the improved PSO algorithm into the ROM look-up table operation while avoiding the multiplication operation. Compensation results can be generated through the performance of a simple data addition operation after the look-up table is introduced. This process significantly increases operation speed. Finally, this method can effectively measure dynamic compensators in real time.

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Digital IIR filter design using particle swarm optimisation

Cited by 84 publications

References 32 publications

Seeker Optimization Algorithm for Several Practical Applications

Seeker Optimization Algorithm for Several Practical Applications

Modified PSO based Adaptive IIR Filter Design for System Identification on FPGA

Online Correction of the Dynamic Errors in a Stored Overpressure Measurement System

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