A truncated normalized max product set of equations and its solution for a recurrent fuzzy neural network
Bibliographic record
Abstract
Defines the truncated normalized max product operation and provides an iterative method for solving a set of equations based on this operation. The operation may serve as the transformation for the set of fully connected units in a fully recurrent network that generally consists of linear threshold units as in a discrete generalized Hopfield network. Component values are not restricted to binary values as would be the case if the network consisted of linear threshold units but can now take on the values in the sets {0,0.1...0.9,1}, {0,0.01,...0.99,1} or similar sets of higher cardinality depending upon the degree of truncation specified. Each unit, although still having discrete output, can provide finer granularity. Due to truncation and normalization the network acting under this transformation has a finite number of states and components of the state vector are bounded. The operation defined can form the basis of transformations in a recurrent network with a finite number of states. This means that fixed points or cycles are possible and the network based on this operation for transformations can be used as an associative memory or pattern classifier with fixed points taking on the role of prototypes.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".