Bibliographic record
Abstract
In mathematics and science, a nonlinear system is a system in which the change of the output is not proportional to the change of input. Nonlinear control systems, which are among the new technologies most widely used in many fields such as economic management, industrial production, technology research and development, ecological prevention and control, are at the core of worldwide automation control technology. In contrast to linear control systems, the nonlinear control system has the characteristics of a data model: stability, zero-input system response, self-excited oscillation or limit cycle, and a more complex structure, increasing the difficulty of its theoretical analysis and technical development. Nonlinear systems are common phenomena in real life and as such cannot be ignored. Analysis and research of nonlinear systems are therefore important, and researchers need to clarify their characteristics, explore scientific and effective application measures, and finally enhance their control quality. This book comprehensively investigates the main principles, core mechanisms, typical problems, and relevant solutions involved in nonlinear systems. In general, this book aims to provide advanced research on nonlinear systems and control schemes for researchers and engineers working in related fields, and thus promote future study in this research area.
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.
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 | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.010 |
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".