Artificial Neural Network Modelling of Rossler's and Chua's Chaotic Systems
Bibliographic record
Abstract
This paper presents the detailed analysis of various Artificial Neural Network (ANN) modelling techniques for chaotic systems. Specifically, Rossler's system and Chua's system are selected for this study for their practical applications, and the outputs of these two systems are used for the ANN training. The Nonlinear Auto-Regressive(NAR) modelling is used for chaotic time series prediction. The Nonlinear Auto-Regressive with Exogenous Inputs (NARX) modelling is used for generating chaotic time series outputs with varying system parameters as exogenous inputs. The research results show that ANN performs well in modelling chaotic systems. Rossler's attractor is modelled using Radial Basis Function Network(RBFN) and a comparative study between FeedForward Neural Network(FFNN) and RBFN is done. The result shows that RBFN uses more neurons to achieve similar training performance compared to FFNN. The 3-layer ANN architecture with hidden neurons varying from 1 to 16 is designed and trained using MATLAB NN toolbox. In a fixed-point FPGA implementation perspective, a study on the performance of ANN modelling of chaotic systems is very relevant.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".