A New Neurodynamics-Based Model for Fuzzy Convex Optimization Problems With Fuzzy Coefficients and General Constraints
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it