Finite-Time Stabilizability and Instabilizability for Complex-Valued Memristive Neural Networks With Time Delays
Bibliographic record
Abstract
This paper studies the stabilizability and instabilizability problems for delayed complex-valued memristive neural networks within finite-time intervals. First, more general assumptions for complex-valued activation functions are given. To check that whether the closed-loop system is stable within a finite-time interval, a novel nonlinear delayed controller with separable real-imaginary parts is designed. It includes two independent parameters different from the existing ones, which makes the controller more general but also leads to great difficulties. To overcome these difficulties, two new inequalities are proposed and proved. Then, through Lyapunov function approach, sufficient conditions are derived for the finite-time stabilizability of the closed-loop system and the settling time is estimated. Accordingly, some criteria for the finite-time instabilizability are also established by adjusting different parameters in the designed controller. Finally, several numerical simulations are given to show the effectiveness and advantages of the proposed results.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".