Application of Response Surface Method and BP Neural Network in the Determination of Tobacco Leaves Tensile Force
Bibliographic record
Abstract
【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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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".