A Comparative Study of Non-Linear Modelling Capabilities of T-S Fuzzy Models and Back-Propagation Neural Networks
Bibliographic record
Abstract
In the era of data-driven decision-making, selecting appropriate nonlinear modeling techniques is critical for building robust and interpretable predictive systems. While both Takagi-Sugeno (T-S) fuzzy models and Back-Propagation (BP) neural networks are well-established universal approximators in the data science domain, their fundamentally different structural characteristics lead to varied performance across application scenarios. This paper presents a systematic comparative study of these two modeling approaches through a series of simulation experiments designed to reflect key tasks in predictive analytics, including static function approximation, dynamic system modeling and forecasting, and real-time state tracking of time-varying systems. By evaluating performance across multiple dimensions modeling accuracy, robustness to noise, and adaptability to temporal dynamics, this work provides actionable insights into the practical strengths and limitations of each model type. The results show that T-S fuzzy models offer superior accuracy in clean, stable environments, while BP neural networks demonstrate strong resilience to noise and generalization ability in uncertain conditions. Additionally, T-S fuzzy models exhibit higher adaptability in real-time, dynamic contexts, making them valuable for time-sensitive predictive applications. This study contributes to the broader data science community by offering a structured framework for model selection based on application-specific demands, helping practitioners and researchers alike to optimize predictive modeling strategies for complex, nonlinear systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".