Optimization of direct action solenoid valve based on CloudPSO

Liu, Qianfeng; Bo, Hanliang; Qin, Benke

doi:10.1016/j.anucene.2012.06.028

Cited by 17 publications

(8 citation statements)

References 6 publications

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“…Haifeng Lu [7] used a preenergizing control strategy based on a dual power supply to accurate control both the time and quantity of the injection events. Qianfeng Liu [8] optimized the structure of direct action solenoid valve based on CloudPSO (Particle Swarm Optimization by Cloud Model), the results proved that the method was feasible and accurate to obtain the key parameters of the solenoid valve to get the highest electromagnetic force. However, the researches mentioned above only evaluate the methods by results, cannot guide the manufactures to analyze and optimize the products in details.…”

Section: Introductionmentioning

confidence: 98%

Power losses and dynamic response analysis of ultra-high speed solenoid injector within different driven strategies

Cheng

Zhang

Xie

2015

Applied Thermal Engineering

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Section: Introductionmentioning

confidence: 98%

Power losses and dynamic response analysis of ultra-high speed solenoid injector within different driven strategies

Cheng

Zhang

Xie

2015

Applied Thermal Engineering

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“…The five-fold cross-validation method was used in the training processes of the five models to avoid the over-fitting problem [30,31], and cloud theory was used to improve the PSO convergence speed [24,25]. In addition, the prediction performances of the five models were verified using a receiver operating characteristic (ROC) curve [32][33][34] and landslide dot density (LDD) [35,36].…”

Section: Introductionmentioning

confidence: 99%

“…In that way, the kernel selection problem is transformed into an optimization problem of kernel parameters and kernel weights in a multiple kernel structure. In recent years, the particle swarm optimization (PSO) algorithm [20][21][22][23] has been widely used to optimize the parameters of intelligent algorithms in many fields [24][25][26][27][28][29]. It is a classic group optimization technique that Kennedy and Eberhart [20,21] designed on the basis of the actions of birds feeding.…”

Section: Introductionmentioning

confidence: 99%

“…At present, studies of the PSO algorithm can be divided into two groups: improvement research and applied research. Most of the improvement research has concentrated on setting the inertia weight and learning factors of the PSO algorithm [22][23][24][25]. The adaptive cloud PSO (CPSO) algorithm is one of the most efficient methods in improvement research.…”

Section: Introductionmentioning

confidence: 99%

“…The adaptive cloud PSO (CPSO) algorithm is one of the most efficient methods in improvement research. It can both increase the convergence speed and ensure the diversity of a population [24,25,28].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Landslide Susceptibility Mapping Based on Particle Swarm Optimization of Multiple Kernel Relevance Vector Machines: Case of a Low Hill Area in Sichuan Province, China

Lin

Xia

Jiang

et al. 2016

IJGI

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Abstract:In this paper, we propose a multiple kernel relevance vector machine (RVM) method based on the adaptive cloud particle swarm optimization (PSO) algorithm to map landslide susceptibility in the low hill area of Sichuan Province, China. In the multi-kernel structure, the kernel selection problem can be solved by adjusting the kernel weight, which determines the single kernel contribution of the final kernel mapping. The weights and parameters of the multi-kernel function were optimized using the PSO algorithm. In addition, the convergence speed of the PSO algorithm was increased using cloud theory. To ensure the stability of the prediction model, the result of a five-fold cross-validation method was used as the fitness of the PSO algorithm. To verify the results, receiver operating characteristic curves (ROC) and landslide dot density (LDD) were used. The results show that the model that used a heterogeneous kernel (a combination of two different kernel functions) had a larger area under the ROC curve (0.7616) and a lower prediction error ratio (0.28%) than did the other types of kernel models employed in this study. In addition, both the sum of two high susceptibility zone LDDs (6.71/100 km 2 ) and the sum of two low susceptibility zone LDDs (0.82/100 km 2 ) demonstrated that the landslide susceptibility map based on the heterogeneous kernel model was closest to the historical landslide distribution. In conclusion, the results obtained in this study can provide very useful information for disaster prevention and land-use planning in the study area.

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Study on electromagnetic force of the new micro digital valve

Yang

Zhang

et al. 2018

Microsyst Technol

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Optimization of direct action solenoid valve based on CloudPSO

Cited by 17 publications

References 6 publications

Power losses and dynamic response analysis of ultra-high speed solenoid injector within different driven strategies

Power losses and dynamic response analysis of ultra-high speed solenoid injector within different driven strategies

Landslide Susceptibility Mapping Based on Particle Swarm Optimization of Multiple Kernel Relevance Vector Machines: Case of a Low Hill Area in Sichuan Province, China

Study on electromagnetic force of the new micro digital valve

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