A Modeling Approach for Energy Saving Based on GA-BP Neural Network

Li, Junke; Guo, Bing; Shen, Yan; Li, Deguang; Huang, Yanhui

doi:10.5370/jeet.2016.11.5.1289

Cited by 16 publications

(6 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, BP neural networks tend to fall into local optimality and cannot find the global optimal point, and the prediction ability depends on the representativeness of sample data. Consequently, it is usually necessary to introduce a genetic algorithm (GA) to optimize the initial weights and thresholds of BP neural networks [34]. A GA can improve the adaptability…”

Section: Prediction With Ga-bp Neural Networkmentioning

confidence: 99%

Prediction of Thermal Conductivity of Litz Winding by Least Square Method and GA-BP Neural Network Based on Numerical Simulations

Dong,

2023

Energies

View full text Add to dashboard Cite

This paper proposes a Litz winding numerical-simulation model considering the transposition effect, and uses the transient-plane-source method to verify the numerical-simulation method. In addition, numerical methods were adopted to further investigate the impact of filling rate and epoxy-resin type, and their combined effects, on thermal conductivity. To facilitate engineering design, the discrete data points were fitted using the least square method to obtain a straightforward and application-friendly polynomial empirical formula. On this basis, the GA-BP neural network was used to analyze the data in order to seek out more accurate prediction results for the entire data set. As a result, compared with the least square method, the error between the prediction result and the target value in the x direction was reduced by 87.04%, and the error in the z direction was reduced by 84.97%.

show abstract

Section: Prediction With Ga-bp Neural Networkmentioning

confidence: 99%

Prediction of Thermal Conductivity of Litz Winding by Least Square Method and GA-BP Neural Network Based on Numerical Simulations

Dong,

2023

Energies

View full text Add to dashboard Cite

show abstract

“…The BPNN is a typical neural network that is widely used in image analysis, industrial signal analysis and processing. The BPNN is a numerical approximation method without establishing mathematical equations (Li et al, 2016). This algorithm has been successfully used to solve many complex nonlinear problems .…”

Section: Back-propagation Neural Networkmentioning

confidence: 99%

Deformation prediction based on an adaptive GA-BPNN and the online compensation of a 5-DOF hybrid robot

Sun

Xiao

Liu

et al. 2020

View full text Add to dashboard Cite

Purpose The purpose of this paper is to accurately obtain the deformation of a hybrid robot and rapidly enable real-time compensation in friction stir welding (FSW). In this paper, a prediction algorithm based on the back-propagation neural network (BPNN) optimized by the adaptive genetic algorithm (GA) is presented. Design/methodology/approach Via the algorithm, the deformations of a five-degree-of-freedom (5-DOF) hybrid robot TriMule800 at a limited number of positions are taken as the training set. The current position of the robot and the axial force it is subjected to are used as the input; the deformation of the robot is taken as the output to construct a BPNN; and an adaptive GA is adopted to optimize the weights and thresholds of the BPNN. Findings This algorithm can quickly predict the deformation of a robot at any point in the workspace. In this study, a force-deformation experiment bench is built, and the experiment proves that the correspondence between the simulated and actual deformations is as high as 98%; therefore, the simulation data can be used as the actual deformation. Finally, 40 sets of data are taken as examples for the prediction, the errors of predicted and simulated deformations are calculated and the accuracy of the prediction algorithm is verified. Practical implications The entire algorithm is verified by the laboratory-developed 5-DOF hybrid robot, and it can be applied to other hybrid robots as well. Originality/value Robots have been widely used in FSW. Traditional series robots cannot bear the large axial force during welding, and the deformation of the robot will affect the machining quality. In some research studies, hybrid robots have been used in FSW. However, the deformation of a hybrid robot in thick-plate welding applications cannot be ignored. Presently, there is no research on the deformation of hybrid robots in FSW, let alone the analysis and prediction of their deformation. This research provides a feasible methodology for analysing the deformation and compensation of hybrid robots in FSW. This makes it possible to calculate the deformation of the hybrid robot in FSW without external sensors.

show abstract

“…Heterogeneous computing for Energy-aware system: Li et al [17] proposes an energy saving approach for GPU using the BP neural networks to guide the DVFS. Paul et al [18] relies on coordinating DVFS for both CPU and GPU to realize a coordinated energy management algorithm for integrated CPU-GPU systems.…”

Section: Related Workmentioning

confidence: 99%

“…It first obtains the number of tasks and corresponing power consumption on each PU, and then formalizes the energy optimization problem as the 0-1 knapsack problem under performance constraints. For getting the number of tasks, we can count the nunmber of task in program, and for getting the power consumption, we can use existing methods [14]- [17]. In solving the global minimization problem, from mathematics perspective, 0-1 programming has been proved to be an effective method to deal with the objective functions and constraints.…”

Section: Introductionmentioning

confidence: 99%

Low-power Scheduling Framework for Heterogeneous Architecture under Performance Constraint

2020

KSII TIIS

View full text Add to dashboard Cite

Today's computer systems are widely integrated with CPU and GPU to achieve considerable performance, but energy consumption of such system directly affects operational cost, maintainability and environmental problem, which has been aroused wide concern by researchers, computer architects, and developers. To cope with energy problem, we propose a task-scheduling framework to reduce energy under performance constraint by rationally allocating the tasks across the CPU and GPU. The framework first collects the estimated energy consumption of programs and performance information. Next, we use above information to formalize the scheduling problem as the 0-1 knapsack problem. Then, we elaborate our experiment on typical platform to verify proposed scheduling framework. The experimental results show that our proposed algorithm saves 14.97% energy compared with that of the time-oriented policy and yields 37.23% performance improvement than that of energy-oriented scheme on average.

show abstract

A Modeling Approach for Energy Saving Based on GA-BP Neural Network

Cited by 16 publications

References 19 publications

Prediction of Thermal Conductivity of Litz Winding by Least Square Method and GA-BP Neural Network Based on Numerical Simulations

Prediction of Thermal Conductivity of Litz Winding by Least Square Method and GA-BP Neural Network Based on Numerical Simulations

Deformation prediction based on an adaptive GA-BPNN and the online compensation of a 5-DOF hybrid robot

Low-power Scheduling Framework for Heterogeneous Architecture under Performance Constraint

Contact Info

Product

Resources

About