Comments on “An intelligent CMAC-PD torque controller with anti-over-learning scheme for electric load simulator”
Bibliographic record
Abstract
This paper points out problems in a paper which appears in the Transactions of the Institute of Measurement and Control entitled “An intelligent CMAC-PD torque controller with anti-over-learning scheme for electric load simulator” by Bo Yang, Huatao Han and Ran Bao (Vol. 39, No. 2, pp.192–200, 2016). Their proposed neural-network weight update makes no intuitive sense: it introduces a term that keeps the output of the neural network close to its input. Here, a standard linear analysis shows that their proposed update applied to adaptive parameters will result in a large steady-state error in general; however for their machine a low steady state error results only because the ideal numerical value of the control signal in Volts happens to be close to the numerical value of the desired input signal in Newton-meters. Furthermore, the authors claim their weight update prevents overlearning, but do not conduct a Lyapunov analysis or even graph a measure of their weights in the results section. This paper shows that a standard Lyapunov analysis (which establishes uniformly ultimately bounded signals for traditional robust update modifications like leakage) fails to reveal a bound on signals for the proposed method. Moreover, simulations demonstrate weight growth that continues at a linear rate during a long simulation when using the proposed method.
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.016 | 0.010 |
| Insufficient payload (model declined to judge) | 0.017 | 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".