PARAMETER OPTIMIZATION OF WHOLE-STRAW RETURNING DEVICE BASED ON THE BP NEURAL NETWORK
Why this work is in the frame
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Bibliographic record
Abstract
ABSTRACT To solve the poor fitting degree of errors in multiobjective parameter optimization and low accuracy, a multiobjective optimization method based on a BP neural network was proposed. By taking the 1ZT-210 type whole-straw returning device as the research object, a BP neural network model on power consumption, straw returning rate and the influencing factors was obtained. By optimizing the model by the proposed method, the optimal parameter combination of the test factors was as follows: the advancing speed of the device was 0.65 km/h, the blade roll rotating speed was 210 rpm, the blade installation angle was 55o, the minimum power consumption was 9.82 kW and the maximum straw returning rate was 93.23%. Under such test conditions, the minimum power consumption was 10.75 kW, and the straw returning rate was 92.46%, which were all better than those obtained by the regression analysis method. Finally, a verification test was conducted on the results of BP neural network optimization. The power consumption of the test was 10.04 kW, the absolute error was 0.22 kW and the relative error was 2.24%. For a straw returning rate of 93.11%, the absolute error was -0.12% and the relative error was 0.13%. The test results indicated that the optimization method was feasible.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.218 | 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 it