SYSTEMATIC ADAPTIVE FUZZY LOGIC MODELLING OF COMPLEX SYSTEMS FROM INPUT-OUTPUT DATA
Bibliographic record
Abstract
The complex nonlinear systems, which are difficult to be mathematically modelled, can be described by a fuzzy model. This article attempts to improve and to address the problems concerning the systematic fuzzy-logic modelling of multi-input-muiti-output (MIMO) systems, by introducing the following three concepts. 1) A generalized and parameterized reasoning mechanism constructed based on the weighted sum of the normalized defuzzified output value of each individual rule. Then the crisp outputs of the fuzzy model can be directly calculated from the crisp inputs using the parameterized reasoning mechanism. This reasoning mechanism is suitable for online learning and real-time control applications. 2) A gradient-descent based parameter adjustment to tune the parameters of reasoning mechanism (which are equal to the number of rules) instead of the existing heuristic complex parameter identification in the literature. 3) An improved method to select the main system input from all input candidates in the presence of singularity. The proposed systematic method of fuzzy modelling has the advantages of simplicity, flexibility, and high accuracy. The two example data, which have been widely used in the literature as benchmark, are used to evaluate the performance of the proposed method.
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 | 0.000 | 0.000 |
| 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 | 0.002 | 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".