Input–output pairing accounting for both structure and strength in coupling
Bibliographic record
Abstract
Input–output pairing is an important problem in control system design and is often performed using the relative gain array (RGA) based approaches. While RGA‐based approaches have been very successful in many applications, they have some well‐known limitations. For example, they may give results which are not consistent with the physical topology since only the strength of interaction between inputs and outputs is taken into account in the RGA. In this work, we propose a new measure for input–output pairing that explores both strength and structural information in input–output coupling. Specifically, we take advantage of the tool of relative degree to measure the physical closeness of input–output pairs and to explore the strength of interaction progressively with respect to the relative degree. We call the proposed measure relative sensitivity array (RSA) between inputs and outputs. Detailed analysis is performed to reveal the relationship between the gain matrix used in the RGA and the sensitivity matrix in the RSA from a mathematical point of view. Since the RSA is an analog of the RGA, many existing pairing guidelines developed for the RGA can be used in the proposed RSA‐based pairing. The proposed RSA‐based approach is applied to two examples. The results show that pairs formed by the proposed approach are consistent with the physical topologies of the processes. Also, the results show that the proposed approach can handle larger systems that cannot be effectively handled by RGA‐based approaches. © 2016 American Institute of Chemical Engineers AIChE J, 63: 1226–1235, 2017
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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 | 0.005 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".