Bibliographic record
Abstract
Neural network structures used for system identification and control are reviewed. Due to the complexity and diversity of the properties of biological neurons, the task of compressing their complicated characteristics into a model is extremely difficult. Toward this goal, an artificial neuron, also called a unit, that receives its inputs from a number of other neurons or from the external world was developed. A weighted sum of these inputs constitutes the argument of an activation function. This is a simple, but useful first approximation of a biological neuron. Using this model, many neural structures, usually referred to as feedforward neural networks, have been reported in the literature. Many of these networks use only present values of inputs, and are therefore called instantaneous or static systems. A natural extension of static networks is the dynamic or recurrent neural network which incorporates feedback in its structure. No general theory for dynamic neural networks has yet developed similar to that for static networks. With the parallel growth in the field of fuzzy logic, many neural models encompassing the principles of neural networks and fuzzy set theory are being developed. An attempt is made to provide the basic concepts of static, dynamic, and fuzzy neural structures.>
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".