Simultaneous computation of model order and parameter estimation for system identification based on opposition-based simulated Kalman filter

Muhammad, Badaruddin; Azmi, Kamil Zakwan Mohd; Ibrahim, Zuwairie; Faudzi, Ahmad Afif Mohd; Pebrianti, Dwi

doi:10.23919/siceiscs.2018.8330163

Cited by 14 publications

(11 citation statements)

References 6 publications

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“…The SKF has also been applied to solve engineering problems. For example, the SKF have employed as feature selector [30][31][32][33], algorithms in adaptive beamforming [34][35][36][37], routing algorithm in manufacturing process [38][39][40] and airport gate allocation [41], tuning algorithm in control engineering [42][43][44][45], and matching algorithm in image processing [46][47][48].…”

Section: Simulated Kalman Filtermentioning

confidence: 99%

Enhancing Simulated Kalman Filter Algorithm using Current Optimum Opposition-based Learning

Azmi¹,

Ibrahim²,

Pebrianti³

et al. 2019

mekatronika

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Simulated Kalman filter (SKF) is a new population-based optimization algorithm inspired by estimation capability of Kalman filter. Each agent in SKF is regarded as a Kalman filter. Based on the mechanism of Kalman filtering, the SKF includes prediction, measurement, and estimation process to search for global optimum. The SKF has been shown to yield good performance in solving benchmark optimization problems. However, the exploration capability of SKF could be further improved. From literature, current optimum opposition-based learning (COOBL) has been employed to increase the diversity (exploration) of search algorithm by allowing current population to be compared with an opposite population. By employing this concept, more potential agents are generated to explore more promising regions that exist in the solution domain. Therefore, this paper intends to improve the exploration capability of SKF through the application of COOBL. The COOBL is employed after the estimation process of SKF. Experimental results over the IEEE Congress on Evolutionary Computation (CEC) 2014 benchmark functions indicate that current optimum opposition-based simulated Kalman filter (COOBSKF) improved the exploration capability of SKF significantly. The COOBSKF also has been compared with five other optimization algorithms and outperforms them all.

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Section: Simulated Kalman Filtermentioning

confidence: 99%

Enhancing Simulated Kalman Filter Algorithm using Current Optimum Opposition-based Learning

Azmi¹,

Ibrahim²,

Pebrianti³

et al. 2019

mekatronika

View full text Add to dashboard Cite

show abstract

“…Other variants called parameter-less SKF and randomized SKF algorithms were proposed in [19][20]. The SKF has also been applied for real world problems like the adaptive beamforming in wireless cellular communication [21][22][23][24], airport gate allocation problem [25][26], feature selection of EEG signal [27][28], system identification [29][30], image processing [31][32], controller tuning [33], and PCB drill path optimization [34][35].…”

Section: Introductionmentioning

confidence: 99%

Single-agent Finite Impulse Response Optimizer vs Simulated Kalman Filter Optimizer

Rahman¹,

Aziz²,

Aziz³

2019

MEKATRONIKA

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Single-agent Finite Impulse Response Optimizer (SAFIRO) is a new estimation-based optimization algorithm which mimics the work procedure of the ultimate unbiased finite impulse response (UFIR) filter. In a real UFIR filter, the horizon length, N, plays an important role to obtain the optimal estimation. In SAFIRO, N represents the repetition number of estimation part that needs to be done in find-ing an optimal solution. On the other hand, Simulated Kalman Filter (SKF) is also an estimation- based optimization algorithm inspired by the estimation capability of Kalman filtering. In literature, substantial amount of works has been devoted to SKF, both in applied research and fundamental enhancements. Thus, in this paper, a performance comparison of both SAFIRO and SKF is presented. It is found that the SAFIRO outperforms the SKF significantly.

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“…If many agents are used, the SKF is called population-based SKF [3], whereas if one agent is used, the SKF is called singlesolution SKF (ssSKF) [4]. To date, the populationbased SKF has been applied in solving many practical problems [5][6][7][8][9][10][11][12][13][14][15][16][17], however, applications of ssSKF is still lacking [18]. In this paper, the usefullness of ssSKF is demonstrated by solving a helical spring design problem.…”

Section: Introductionmentioning

confidence: 99%

Design of an Helical Spring using Single-solution Simulated Kalman Filter Optimizer

Azzam¹,

Batool²,

Fauzi³

2019

MEKATRONIKA

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Optimization is one of the important process in solving engineering problems. Regrettably, there are numerous problems in practical optimization that cannot be solved flawlessly within reasonable computational effort. Thus, metaheuristic approach is often useful to get near-optimal solution when the best solution is not achievable. This paper demonstrates the usefullness of a metaheuristic algorithm called single-solution simulated Kalman filter (ssSKF) in helical spring design, which is an example of structural engineering design problem. The ssSKF is a single agent-based optimization algorithm based on the Kalman filtering. The solution obtained by the ssSKF is compared againsts the genetic algorithm, co-evolutionary particle swarm optimization, co-evolutionary differential evolution, bat algorithm, and artificial bee colony.

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Simultaneous computation of model order and parameter estimation for system identification based on opposition-based simulated Kalman filter

Cited by 14 publications

References 6 publications

Enhancing Simulated Kalman Filter Algorithm using Current Optimum Opposition-based Learning

Enhancing Simulated Kalman Filter Algorithm using Current Optimum Opposition-based Learning

Single-agent Finite Impulse Response Optimizer vs Simulated Kalman Filter Optimizer

Design of an Helical Spring using Single-solution Simulated Kalman Filter Optimizer

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