Neural Network: A Potential Approach for Error Reduction in Color Values of Polycarbonate
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 (R2), 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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".