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Operating Comfort Prediction Model of Human-Machine Interface Layout for Cabin Based on GEP.

Deng L, Wang G, Chen B - Comput Intell Neurosci (2015)

Bottom Line: Through joint angles to describe operating posture of upper limb, the joint angles are taken as independent variables to establish the comfort model of operating posture.With 22 groups of evaluation data as training sample and validation sample, GEP algorithm is used to obtain the best fitting function between the joint angles and the operating comfort; then, operating comfort can be predicted quantitatively.The operating comfort prediction result of human-machine interface layout of driller control room shows that operating comfort prediction model based on GEP is fast and efficient, it has good prediction effect, and it can improve the design efficiency.

View Article: PubMed Central - PubMed

Affiliation: School of Mechatronic Engineering, Southwest Petroleum University, Chengdu 610500, China.

ABSTRACT
In view of the evaluation and decision-making problem of human-machine interface layout design for cabin, the operating comfort prediction model is proposed based on GEP (Gene Expression Programming), using operating comfort to evaluate layout scheme. Through joint angles to describe operating posture of upper limb, the joint angles are taken as independent variables to establish the comfort model of operating posture. Factor analysis is adopted to decrease the variable dimension; the model's input variables are reduced from 16 joint angles to 4 comfort impact factors, and the output variable is operating comfort score. The Chinese virtual human body model is built by CATIA software, which will be used to simulate and evaluate the operators' operating comfort. With 22 groups of evaluation data as training sample and validation sample, GEP algorithm is used to obtain the best fitting function between the joint angles and the operating comfort; then, operating comfort can be predicted quantitatively. The operating comfort prediction result of human-machine interface layout of driller control room shows that operating comfort prediction model based on GEP is fast and efficient, it has good prediction effect, and it can improve the design efficiency.

No MeSH data available.


Comparison chart of actual value and predicted value by GEP and BP prediction model.
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fig12: Comparison chart of actual value and predicted value by GEP and BP prediction model.

Mentions: Back propagation neural network model and GEP algorithm model are used to predict the above 22 sets of data, respectively, and the predicted values and the relative error are shown in Table 8. The comparison chart of actual value and predicted value by two kinds of prediction model is shown in Figure 12. In Table 8, the average relative error of operating comfort prediction obtained by GEP model and BP model is 0.37% and 1.89%, respectively. In comparison, the average relative error of GEP model is smaller and the prediction accuracy is higher. So, the GEP model has high fitting degree.


Operating Comfort Prediction Model of Human-Machine Interface Layout for Cabin Based on GEP.

Deng L, Wang G, Chen B - Comput Intell Neurosci (2015)

Comparison chart of actual value and predicted value by GEP and BP prediction model.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4581542&req=5

fig12: Comparison chart of actual value and predicted value by GEP and BP prediction model.
Mentions: Back propagation neural network model and GEP algorithm model are used to predict the above 22 sets of data, respectively, and the predicted values and the relative error are shown in Table 8. The comparison chart of actual value and predicted value by two kinds of prediction model is shown in Figure 12. In Table 8, the average relative error of operating comfort prediction obtained by GEP model and BP model is 0.37% and 1.89%, respectively. In comparison, the average relative error of GEP model is smaller and the prediction accuracy is higher. So, the GEP model has high fitting degree.

Bottom Line: Through joint angles to describe operating posture of upper limb, the joint angles are taken as independent variables to establish the comfort model of operating posture.With 22 groups of evaluation data as training sample and validation sample, GEP algorithm is used to obtain the best fitting function between the joint angles and the operating comfort; then, operating comfort can be predicted quantitatively.The operating comfort prediction result of human-machine interface layout of driller control room shows that operating comfort prediction model based on GEP is fast and efficient, it has good prediction effect, and it can improve the design efficiency.

View Article: PubMed Central - PubMed

Affiliation: School of Mechatronic Engineering, Southwest Petroleum University, Chengdu 610500, China.

ABSTRACT
In view of the evaluation and decision-making problem of human-machine interface layout design for cabin, the operating comfort prediction model is proposed based on GEP (Gene Expression Programming), using operating comfort to evaluate layout scheme. Through joint angles to describe operating posture of upper limb, the joint angles are taken as independent variables to establish the comfort model of operating posture. Factor analysis is adopted to decrease the variable dimension; the model's input variables are reduced from 16 joint angles to 4 comfort impact factors, and the output variable is operating comfort score. The Chinese virtual human body model is built by CATIA software, which will be used to simulate and evaluate the operators' operating comfort. With 22 groups of evaluation data as training sample and validation sample, GEP algorithm is used to obtain the best fitting function between the joint angles and the operating comfort; then, operating comfort can be predicted quantitatively. The operating comfort prediction result of human-machine interface layout of driller control room shows that operating comfort prediction model based on GEP is fast and efficient, it has good prediction effect, and it can improve the design efficiency.

No MeSH data available.