A New Neurodynamics-Based Model for Fuzzy Convex Optimization Problems With Fuzzy Coefficients and General Constraints
Bibliographic record
Abstract
Fuzzy convex optimization problems with fuzzy coefficients (FCOPFCs) arise in many applications. Although many neurodynamics-based models have been proposed for solving FCOPFCs, most of them are designed for FCOPFCs with equality or inequality constraints only. However, in many applications, the FCOPFCs often come with both equality and inequality constraints (general constraints, for short), so most of the neurodynamics-based models no longer work in these situations. Therefore, this article aims to construct a new model for FCOPFCs with general constraints to extend the applications of neurodynamics-based models. First of all, based on fuzzy set theory, the original FCOPFCs with general constraints is transformed into a series of interval programming tasks and further transformed into crisp optimization problems with weights. Then, a novel continuous-time neurodynamics-based model with a single-layer structure is established to solve the crisp optimization problem with weights. Further, we discuss the global existence and prove the stability of state solutions. The theoretical results show that the state solutions reach the feasible region within finite time and converge to the optimal solution with the smallest 2-norm. Simulation results completed for three kinds of FCOPFCs show the validity of the approach, and the results in real-world applications demonstrate the excellent performance of the proposed model.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".