Analysis and optimization of noises of an analog circuit via PSO algorithms

Singh, Chabungbam Lison; Baishnab, Krishna Lal; Anandini, Ch.

doi:10.1007/s00542-017-3573-8

Cited by 7 publications

(2 citation statements)

References 44 publications

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“…For actual systems with uncertainties, a strong tracking Kalman filter (Ma et al, 2016) has been proposed. Although some intelligent optimization algorithms, such as particle swarm optimization (PSO) (Singh et al, 2019) and the whale optimization algorithm (WOA) (Gandomi and Kashani, 2017), have been applied to stochastic parameter optimization, these algorithms can have low optimization accuracy and slow convergence speed and are easily trapped in local optimal solutions.…”

Section: Introductionmentioning

confidence: 99%

Optimized adaptive filtering for fault estimation of the dynamics of high-speed train based on robust extended Kalman filter

Liang

Wang

et al. 2023

Transactions of the Institute of Measurement and Control

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In this paper, an optimized adaptive robust extended Kalman filter is proposed based on random weighting factors and an improved whale optimization algorithm for fault estimation of the dynamics of high-speed trains with constant time delays, drastically changing noise and stochastic uncertainties. Robust upper bounds are proposed to improve the performance of the extended Kalman filter by decreasing the influence of the linearization error on filtering for the dynamics of high-speed trains with constant time delays, and its robustness is proven to guarantee the feasibility of the proposed upper bounds. Furthermore, considering drastically changing noise with unknown statistics, a random weighting adaptive algorithm is proposed to implement unbiased noise estimation so that the robust extended Kalman filter can still be implemented well. In addition, a differential evolution algorithm and adaptive parameter are introduced to improve the performance of the whale optimization algorithm so that the stochastic uncertainties are optimized, and the influence of the stochastic uncertainties on filtering is further decreased. The simulation results in the three conditions show that, compared with the variational Bayes adaptive iterated extended Kalman filter, using the proposed method, the position, speed and fault estimation errors are decreased by 31.8%, 33.2% and 28.3%, respectively, on average, which depends on more accurate noise estimation.

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Section: Introductionmentioning

confidence: 99%

Optimized adaptive filtering for fault estimation of the dynamics of high-speed train based on robust extended Kalman filter

Liang

Wang

et al. 2023

Transactions of the Institute of Measurement and Control

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“…Radio-frequency circular integrated inductors sizing optimization using … (Imad El Hajjami) 6321 such as artificial bee colony (ABC) [8], [9], genetic algorithms (GA) [10], [11], tabu search (TS) [12], simulated annealing (SA) [13], differential evolution (DE) [14], [15], ant colony optimization (ACO) [16], [17], Jaya algorithm (JA) [18], particle swarm optimization (PSO) [19], [20], salp swarm algorithm (SSA) [21], firefly algorithm (FA) [22], and the gravitational search algorithm (GSA) [23].…”

Section: Introductionmentioning

confidence: 99%

Radio-frequency circular integrated inductors sizing optimization using bio-inspired techniques

Hajjami

Benhala

2022

IJECE

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<span lang="EN-US">In this article, a comparative study is accomplished between three of the most used swarm intelligence (SI) techniques; namely artificial bee colony (ABC), ant colony optimization (ACO), and particle swarm optimization (PSO) to carry out the optimal design of radio-frequency (</span><span lang="EN-US">RF) spiral inductors, the three algorithms are applied to the cost function of RF circular inductors for 180 nm beyond 2.50 GHz, the aim is to ensure optimal performance with less error in inductance, and a high-quality factor when compared to electromagnetic simulation. Simulation experiments are achieved and performances regarding convergence velocity, robustness, and computing time are checked. Also, this paper shows an impact study of technological parameters and geometric features on the inductance and the quality factor of the studied integrated inductor. The building method of constraints design with algorithms used has given good results and electromagnetic simulations are of good accuracy with an error of 2.31% and 4.15% on the quality factor and inductance respectively. The simulation shows that ACO provides more accuracy in circuit size and fewer errors than ABC and PSO, while PSO and ABC are better in terms of convergence velocity.</span>

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