Neo-Fuzzy Integrated Adaptive Decayed Brain Emotional Learning Network for Online Time Series Prediction
Bibliographic record
Abstract
Adaptive decayed brain emotional learning (ADBEL) network is recently proposed for the online time series forecasting problems. As opposed to other popular learning networks, such as multilayer perceptron, adaptive neuro-fuzzy inference system, and locally linear neuro-fuzzy model, ADBEL network offers lower computational complexity and fast learning, which make it an ideal candidate for the time series prediction in an online fashion. In fact, these prominent features are inherited from the mechanism employed by the limbic system of the mammalian brain in processing the external stimuli, which also forms the basis of the ADBEL network. This paper aims at further enhancing the forecasting performance of the ADBEL network through its integration with a neo-fuzzy network. The selection of the neo-fuzzy network is made as it offers features required for online prediction in real time environments including simplicity, transparency, accuracy, and lower computational complexity. Furthermore, this integration is only considered in the orbitofrontal cortex section of the ADBEL network and only three membership functions are employed to realize the neo-fuzzy neuron. Thus, the resultant neo-fuzzy integrated ADBEL (NF-ADBEL) network is still simple and can be deployed in online prediction problems. Few chaotic time series namely the Mackey glass, Lorenz, Rossler, and the Disturbance storm time index as well as the Narendra dynamic plant identification problem are used to evaluate the performance of the proposed NF-ADBEL network in terms of the root mean squared error and correlation coefficient criterions using MATLAB®programming environment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".