Rotor Position Self-Sensing of SRM Using PSO-RVM

Xiang, Qianwen; Yuan, Ye; Yu, Yinquan; Chen, Kunhua

doi:10.3390/en11010066

Cited by 6 publications

(4 citation statements)

References 13 publications

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“…As for RBF kernel function, the kernel width

σ

has an important influence on the prediction performance and accuracy of RVM. PSO has the advantages of simplicity, fast convergence, low computational complexity, and good global search ability 42 . Hence, kernel widths

σ_{1}

and

σ_{2}

of two RVM models are optimized by adopting PSO algorithm.…”

Section: Proposed Lube Methods Based On Pso‐rvmmentioning

confidence: 99%

“…PSO has the advantages of simplicity, fast convergence, low computational complexity, and good global search ability. 42 Hence, kernel widths σ 1 and σ 2 of two RVM models are optimized by adopting PSO algorithm. During the iteration of PSO, the optimal kernel parameters of RVM model can be obtained by minimizing CWC; then, the optimal RVM models are used for predicting the lower bound and the upper bound of early warning threshold (WI) of the input variable, respectively, and the output variables y 1 and y 2 of two RVM models are the upper bound and the lower bound of predictive warning threshold for the corresponding future unknown time-domain feature, respectively.…”

Section: Psomentioning

confidence: 99%

See 1 more Smart Citation

Interval early warning method for state of engineering structures based on structural health monitoring data

Liu

Qin

Sun

et al. 2022

Structural Contr & Hlth

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In order to carry out early warning for the abnormal state of engineering structures in time and optimize early warning interval (WI), a new construction of WI method based on relevance vector machine (RVM) and particle swarm optimization (PSO) under the framework of lower upper bound estimation (LUBE) is proposed. First, extract time-domain features of structural monitoring data and then two RVM models are trained using time-domain features to construct lower and upper bound of WI. Kernel parameters of RVM are optimized by PSO algorithm to minimize the loss function of LUBE and obtain the best optimal evaluation indices of WI. The LUBE based on PSO-RVM interval warning method is verified through a numerical simulation and a case study on structural monitoring data of quayside container crane (QCC), and the performance of WI is compared with other prediction methods. The results indicate that the proposed construction of WI method can effectively improve the quality of WI, realize early warning damage, and provide better performance for early WI.

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“…As for RBF kernel function, the kernel width

σ

σ_{1}

and

σ_{2}

of two RVM models are optimized by adopting PSO algorithm.…”

Section: Proposed Lube Methods Based On Pso‐rvmmentioning

confidence: 99%

Section: Psomentioning

confidence: 99%

Interval early warning method for state of engineering structures based on structural health monitoring data

Liu

Qin

Sun

et al. 2022

Structural Contr & Hlth

View full text Add to dashboard Cite

show abstract

“…For the problem of parameter optimization, intelligent optimization algorithms are introduced to determine the optimal parameters. Many scholars adopted different intelligent optimization algorithms to optimize the parameters of RVM, including the ant lion optimizer [43], whale optimization algorithm [44], imperialist competitive algorithm [45], cuckoo search optimization [46], particle swarm optimization [47], gravitational search optimization [48], and quantum-inspired gravitational search [49]. The grasshopper optimization algorithm (GOA), proposed by Saremi, is a new swarm intelligence algorithm [50].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Construction Simulation Coupling a Concrete Temperature Field Interval Prediction Model with Optimized Hybrid-Kernel RVM for Arch Dams

et al. 2020

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Joint grouting simulation is an important aspect of arch dam construction simulation. However, the current construction simulation model simplifies the temperature factors in joint grouting simulation, which leads to the difference between the simulation results and the actual construction schedule. Furthermore, the majority of existing temperature prediction research is based on deterministic point predictions, which cannot quantify the uncertainties of the prediction values. Thus, this study presents a real-time construction simulation method coupling a concrete temperature field interval prediction model to address these problems. First, a real-time construction simulation model is established. Secondly, this paper proposes a concrete temperature interval prediction method based on the hybrid-kernel relevance vector machine (HK-RVM) with the improved grasshopper optimization algorithm (IGOA). The hybrid-kernel method is adopted to ensure the prediction accuracy and generalization ability of the model. Additionally, the improved grasshopper optimization algorithm (IGOA), which utilizes the tent chaotic map and cosine adaptive method to improve the algorithm performance, is developed for the parameter optimization of HK-RVM. Thirdly, concept drift detection based on variable window technology is proposed to update the prediction model. Finally, an arch dam project in China is used as a case study, by which the superiority and applicability of the proposed method are proven.

show abstract

“…Therefore, in this study, a relevance vector machine (RVM) and sequential floating forward search (SFFS) are applied to treat the aforementioned classification problem. Because the RVM is a non-linear probability model [26] and a Bayesian sparse kernel technique, which provides posterior probabilities, it shares many properties with an SVM.…”

Section: Introductionmentioning

confidence: 99%

An Optimal Burn-In Policy for Cellular Phone Lithium-Ion Batteries Using a Feature Selection Strategy and Relevance Vector Machine

Yang

Tang

et al. 2018

Energies

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The early detection of defective lithium-ion batteries in cellular phones is critical due to the rapid increase in popularity and mass production of cellular phones. It is essential for manufacturers to design an optimal burn-in policy to differentiate between normal and weak batteries in short cycles prior to shipping them to the marketplace. A novel approach to determine the optimal burn-in policy using a feature selection strategy and relevance vector machine (RVM) is proposed. The sequential floating forward search (SFFS) is used as the feature selection method to find an optimal feature subset from the entire sequence of the batteries’ quality characteristics while preserving the original variables. Given the selected feature subset, the RVM is applied to classify batteries into two groups and simultaneously obtain the posterior probabilities. To achieve better discrimination performance with less risk, a new characteristic is extracted from the discharge profile. Subsequently, an optimization cost model is developed by introducing a classification instability penalty to ensure the stability of the optimal number of burn-in cycles. A case study utilizing cellular phone lithium-ion batteries randomly selected from manufactured lots is presented to illustrate the proposed methodology. Furthermore, we conduct a comparison with the cumulative degradation (CD) method and non-cumulative degradation (NCD) method based on the Wiener process. The results show that our proposed burn-in test method performs better than comparable methods.

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Rotor Position Self-Sensing of SRM Using PSO-RVM

Cited by 6 publications

References 13 publications

Interval early warning method for state of engineering structures based on structural health monitoring data

Interval early warning method for state of engineering structures based on structural health monitoring data

Real-Time Construction Simulation Coupling a Concrete Temperature Field Interval Prediction Model with Optimized Hybrid-Kernel RVM for Arch Dams

An Optimal Burn-In Policy for Cellular Phone Lithium-Ion Batteries Using a Feature Selection Strategy and Relevance Vector Machine

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