Application of Response Surface Method and BP Neural Network in the Determination of Tobacco Leaves Tensile Force
Notice bibliographique
Résumé
【Objective】The tensile force of tobacco leaves is one of the physical characteristics of tobacco leaves, which reflects the processing resistance of tobacco leaves. Studying the tensile force characteristics of tobacco leaves can provide reference for the setting of processing parameters of threshing and redrying tobacco leaves, and further improve the economic benefits of tobacco processing.【Method】In order to improve the stability and accuracy of measuring the tensile force of tobacco leaves by texture analyzer, three factors and three levels parameters were designed by Box-Behnken principle, and the influence of each parameter on the coefficient of variation of the results was analyzed by response surface method, and the optimal parameter combination for measuring the tensile force was obtained. The effect of moisture content on tobacco leaf tension was studied. Further, the BP neural network prediction model of moisture content X- tension Y of tobacco leaves was established.【Result】The analysis results of response surface method show that it can be seen that the sample width has a significant influence on the coefficient of variation of tensile force, and the test rate has a significant influence, but the trigger force has no obvious influence. The optimal parameter combination was obtained: the sample width was 10 mm, the test rate was 0.5 mm/s, the trigger force was 0.1 N. The coefficient of variation of the tensile force measured by these parameters decreased significantly to 13.8%. With the increase of moisture content, the tensile strength of tobacco leaves first increased and then decreased. When the moisture content of Jingdong C3F was 18.41%, the tensile strength reached the maximum, which was 0.456 N/mm. The tensile strength of Jingdong C1F was only 0.288 N/mm, when the moisture content was 18.46%. When the moisture content of Honghe C3F and Pu 'er C3F were 20.64% and 18.47%, the maximum tensile strength were 0.447 N/mm and 0.310 N/mm respectively. There are differences in the tension of tobacco leaves in different regions and grades. The BP neural network prediction model of moisture content X- tension Y of tobacco leaves was established. The predicted value was in good agreement with the real value, with the mean square error MSE of 0.04761 and the root mean square error RMSE of 0.2182.【Conclusion】Response surface analysis can be used to analyze the influence of parameters on the results of tobacco tensile test, and the stability of the results is improved after the parameters are optimized. The tensile force of tobacco leaves in different regions and grades is significantly different, and it first increases and then decreases with the increase of moisture content. According to this law, the appropriate moisture content can be selected to make tobacco leaves have the best processing resistance. The established BP neural network model has small error and good accuracy, and can be used to predict the tensile force of tobacco leaves.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,009 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».