Improving the Efficacy of Artificial Neural Network Training by Optimizing Training Data for the Simulation and Prediction of Electroencephalogram Chaotic Patterns
Bibliographic record
Abstract
In this paper, the quality and size of training data are investigated for improving the training efficacy of artificial neural network (ANN) to generate Lorenz chaotic system and predict the time series outputs using Nonlinear Auto-Regressive (NAR) model. The designed NAR ANN model will be used for the simulation and analysis of Electroencephalogram (EEG) signals captured from brain activities. A simple ANN topology with a single hidden layer is used, and different ANN architectures with varying number of hidden neurons (n=3 to 16) and input delays (d=1 to 4) are trained with Levenberg-Marquardt algorithm using the MATLAB Neural Network Toolbox. The training results are investigated by comparing two aspects of the training data: size and precision. It is found that for any given ANN architecture, the training performance cannot be improved by solely increasing the training data size in the case of Lorenz system, which is useful knowledge towards reducing the training data size of EEG signals required for training ANN-based NAR model. On the other hand, the training performance can be improved by training data with the same size but better precision. Moreover, when training data with the same size and precision is used, the training performance varies depends on the segment of the Lorenz chaotic trajectory used for the training and can worsen if the changing rate of the selected segment represented by the training data is high.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".