Wavelet networks: an alternative to classical neural networks
Why this work is in the frame
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Bibliographic record
Abstract
Artificial neural networks (ANNs) are being widely used to predict and forecast highly nonlinear systems. Recently, Wavelet networks (WNs) have been shown to be a promising alternative to traditional neural networks. In this study, the robustness of WNs and ANNs in modeling two distinct time series is investigated. The first series represents a chaotic system (Henon map) and the second series represents a stochastic geophysical time series (streamflows). Monthly streamflow values of the English river between Umferville and Sioux Lookout, ON, Canada, are considered in this study. For the implementation of traditional ANNs, the weights and bias values are optimized using genetic algorithms (GAs). However, in WNs, along with weights and bias, the translation and dilation factors of wavelets are also optimized. Use of GAs to optimize the network parameters is to overcome the problem of convergence towards local optima. Results from the study indicate that, WNs are more suitable for modeling short time high frequency time series like Henon map. However, performance of WNs is comparable with that of ANNs in modeling low frequency time series like streamflows.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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