Implementation of an adaptive intelligent controller for benchmark thermal system
Bibliographic record
Abstract
In this work, a neural network (NN) based adaptive controller is developed and implemented for precise temperature control of a benchmark thermal system in cold climate. The newly devised NN controller is capable of overcoming the limitations of model dependent conventional fixed gain temperature controllers. The proposed NN controller is designed using the combination of off-line and on-line trainings of the feed-forward neural network. The transient and steady-state behaviors of the proposed NN-based thermal control system for central heating are improved by incorporating a unique feature of adaptive learning which aids the on-line robust temperature control over a wide operating range. The stability of the proposed NN-based thermal system has been ensured by a combination of off-line and on-line trainings of the NN. As an integral part of this work, efforts have been directed for the real-time implementation of the NN-based thermal system using a digital signal processor (DSP) controller board ds1102. A series of tests have been carried out in order to evaluate the performances of the NN-based benchmark thermal system for central heating. The laboratory test results validate the efficiency of the NN controller as an adaptive controller in the high performance benchmark thermal systems.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".