Neural Network: A Potential Approach for Error Reduction in Color Values of Polycarbonate
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Bibliographic record
Abstract
ABSTRACT In current exploration, the artificial neural network (ANN) is executed to reduce the errors in color values of polycarbonate. The network consists of sigmoid hidden units and a linear output unit arranged in a feed forward backpropagation architecture. An optimal design is accomplished for 10, 12, 14, 16, 18, and 20 hidden neurons on a hidden layer with five different algorithms involving batch gradient descent, batch variable learning rate, resilient back propagation, scaled conjugate gradient, and Levenberg–Marquardt. The training data for ANN are obtained from experimental measurements. There were 22 inputs and three tristimulus color values L *, a *, and b * were used as an output layer. Statistical analysis in terms of root‐mean‐squared, an absolute fraction of variance ( R 2 ), as well as a mean square error is used to investigate the performance of ANN. The best result in terms of statistics is presented by the LM algorithm with 14 neurons in the designed ANN model. The degree of accuracy of the ANN model in reduction of errors is proven acceptable in all statistical analysis and shown in results. However, it was concluded that ANN provides a possible method in error reduction in specific color tristimulus values. © 2013 Wiley Periodicals, Inc. Adv Polym Technol 2014, 33, 21402; View this article online at wileyonlinelibrary.com . DOI 10.1002/adv.21402
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it