An Intelligent Particle Filter With Adaptive M-H Resampling for Liquid-Level Estimation During Silicon Crystal Growth

Zhang, Xinyu; Liu, Ding; Yang, Yuan; Liang, Junli

doi:10.1109/tim.2020.3026760

Cited by 10 publications

(3 citation statements)

References 31 publications

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“…(1) Te same job can only be processed on one equipment at any time (2) Only one job can be processed by the same equipment at any time (3) Te single crystal furnace cannot be interrupted during the production process, but can wait at a specifc process (4) Te same job can only be processed in the next operation after the previous operation is completed (5) Assuming that the failure factor is not considered, all single crystal furnaces are able to work normally Te symbols and defnitions involved in this study are shown in Table 2.…”

Section: Te Model Of the Silicon Single Crystal Batch Schedulingmentioning

confidence: 99%

“…It is recognized as one of the most complex manufacturing systems [2]. To break the bottleneck of foreign monopoly technology and have its own core technology, many domestic scholars are committed to improving the growth quality of silicon single crystals [3][4][5]. In recent years, many enterprises have conquered large-scale silicon single crystals and entered the preliminary mass production stage.…”

Section: Introductionmentioning

confidence: 99%

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Two‐Stage Hybrid Optimization Algorithm for Silicon Single Crystal Batch Scheduling Problem under Fuzzy Processing Time

Kang

Liu

et al. 2023

Mathematical Problems in Engineering

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Considering the widely existing processing time uncertainty in the real-world production process, this paper constructs a fuzzy mathematical model for the silicon single crystal production batch scheduling problem to minimize the maximum completion time. In this paper, a two-stage hybrid optimization algorithm (TSHOA) is proposed for solving the scheduling model. Firstly, the improved differential evolution algorithm (IDE) is used to solve the order quantity allocation problem of silicon single crystal with different sizes to obtain the quantity of silicon single crystal rods with different sizes produced by different types of single crystal furnaces. Secondly, the variable neighborhood search (VNS) algorithm is adopted to optimize the order quantity sequencingof batch production processes. Finally, simulations and comparisons demonstrate the feasibility of the model and the effectiveness of TSHOA.

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Section: Te Model Of the Silicon Single Crystal Batch Schedulingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Two‐Stage Hybrid Optimization Algorithm for Silicon Single Crystal Batch Scheduling Problem under Fuzzy Processing Time

Kang

Liu

et al. 2023

Mathematical Problems in Engineering

Self Cite

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“…As the resampling strategy improves, the number of parameters to set increases. The paper [ 23 ] proposed an adaptive Metropolis–Hastings (M–H) resampling algorithm, which introduced the accept–reject mechanism of M–H into resampling. It adaptively selected Gaussian mutation or crossover of the high weight and low weight to resample particles according to the particle distribution, effectively improving the diversity of the particles.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Cost-Reference Particle Filter with Resampling of Multi-Population Cooperation

Zhang

Ren

Duan

et al. 2023

Sensors

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Although the cost-reference particle filter (CRPF) has a good advantage in solving the state estimation problem with unknown noise statistical characteristics, its estimation accuracy is still affected by the lack of particle diversity and sensitivity to the particles’ initial value. In order to solve these problems of the CRPF, this paper proposed an intelligent cost-reference particle filter algorithm based on multi-population cooperation. A multi-population cooperative resampling strategy based on ring structure was designed. The particles were divided into multiple independent populations upon initialization, and each population generated particles with a different initial distribution. The particles in each population were divided into three different particle sets with high, medium and low weights by the golden section ratio according to the weight. The particle sets with high and medium weights were retained. Then, a cooperative strategy based on Gaussian mutation was designed to resample the low-weight particle set of each population. The high-weight particles of the previous population in the ring structure were randomly selected for Gaussian mutation to replace the low-weight particles in the current population. The low-weight particles of all populations were resampled in turn. The simulation results show that the intelligent CRPF based on multi-population cooperation proposed in this paper can reduce the sensitivity of the CRPF to the particles’ initial value and improve the particle diversity in resampling. Compared with the general CRPF and intelligent CRPF with adaptive MH resampling (MH-CRPF), the RMSE and MAE of the proposed method are lower.

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Sequential Abruptly Changing Hidden States Estimation using Adaptive Particle Impoverishment Mitigation Scheme

Kuptametee

Aunsri

2023

2023 Joint International Conference on Digital Arts, Media and Technology With ECTI Northern Section Conference on Electrical,

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An Intelligent Particle Filter With Adaptive M-H Resampling for Liquid-Level Estimation During Silicon Crystal Growth

Cited by 10 publications

References 31 publications

Two‐Stage Hybrid Optimization Algorithm for Silicon Single Crystal Batch Scheduling Problem under Fuzzy Processing Time

Two‐Stage Hybrid Optimization Algorithm for Silicon Single Crystal Batch Scheduling Problem under Fuzzy Processing Time

An Intelligent Cost-Reference Particle Filter with Resampling of Multi-Population Cooperation

Sequential Abruptly Changing Hidden States Estimation using Adaptive Particle Impoverishment Mitigation Scheme

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