Expanded Neo-Fuzzy Adaptive Decayed Brain Emotional Learning Network for Online Time Series Predication
Bibliographic record
Abstract
The Neo-Fuzzy integrated Adaptive Decayed Brain Emotional Learning (NF-ADBEL) network has recently been proposed for online time series predicting problems. The NF-ADBEL network is suitable for online time series prediction with shorter update intervals and offers features such as fast learning, accuracy, simplicity, and lower computational complexity. However, the neo-fuzzy neuron network in NF-ADBEL was integrated only in the orbitofrontal cortex (OFC) part of the ADBEL network. This paper aims to further improve the performance of the NF-ADBEL network by integrating the neo-fuzzy neuron network into the amygdala (AMY) section as well, inspired by a fully integrated version of a neo-fuzzy-based pattern recognizer. As is known, the AMY has two outputs: one response is based on imprecise information received from the thalamus, and the second response is based on information received from the sensory cortex. In this study, the imprecise response generation is operated as previously, while the other AMY process is treated by neo-fuzzy neurons. The resultant network is called Expanded Neo-Fuzzy integrated Adaptive Decayed Brain Emotional Learning (ENF-ADBEL). The modified network is still simple and meets the requirement for online prediction problems. A few chaotic and stochastic nonlinear systems, namely the Mackey-Glass, Lorenz, Rossler, disturbance storm time index, Narendra dynamic plant identification, wind speed and wind power series, are used to evaluate the performance of the proposed network in terms of the root mean squared error (RMSE) and correlation coefficient (COR) criteria in a MATLAB programming environment.
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How this classification was reachedexpand
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 | 0.001 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".