A Wiener Neural Network-Based Identification and Adaptive Generalized Predictive Control for Nonlinear SISO Systems
Bibliographic record
Abstract
In this study, a Wiener-type neural network (WNN) is derived for identification and control of single-input and single-output (SISO) nonlinear systems. The nonlinear system is identified by the WNN, which consists of a linear dynamic block in cascade with a nonlinear static gain. The Lipschitz criteria for model order determination and back propagation for the adjustment of weights in the network are presented. Using the parameters of the Wiener model, the analytical expressions used in the controller, generalized predictive control (GPC) is modified every time step, to handle the nonlinear dynamics of the controlled variable. Finally, the proposed WNN-based GPC algorithm is tested in simulation on several nonlinear plants with different degrees of nonlinearity. Simulation results show that WNN identification approach has better accuracy, in comparison to other neural network identifiers. The WNN-based GPC has better control performance, in comparison to standard GPC.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".