Abstract
The number of fuzzy rules in a fuzzy neural network usually depends on the grid-type partition or clustering-based partition. However, the number of grids or clusters needs to be set in advance and the initial number of fuzzy sets on each dimension is usually the same. This usually does not fit the real situation (for example, when forecasting the weather, various features such as temperature, humidity, wind direction, wind speed, etc., may have different numbers of fuzzy sets). In this paper, a novel zero-order fuzzy neural network with an adaptive fuzzy partition (AdFPFNN) is proposed, which does not need to preset the number of fuzzy sets and owns various numbers of fuzzy sets on different dimensions. The trick is to use the mean shift algorithm to perform adaptive clustering for each feature individually. For common fuzzy neural networks, dealing with high-dimensional problems is challenging work on account of the “fuzzy rule curse” and “computation underflow”. To overcome these issues, an improved version of AdFPFNN is devised to tackle high-dimensional problems, called PaCoAMF-based AdFPFNN. It adopts two techniques to overcome the “fuzzy rule curse” and “computation underflow”. One is the construction of a partially combined fuzzy rule base, which can generate rules that are adequate but do not grow exponentially with the number of features. Another is the proposal of an adaptive membership function that can guarantee the elimination of numerical underflow due to the product T-norm. Without feature selection or other dimensional reduction methods, the proposed PaCoAMF-based AdFPFNN can be directly used to solve high-dimensional problems. Simulation results on one constructed dataset and 13 real-world datasets (including 5 high-dimensional problems) confirm the effectiveness of the proposed models.
| Original language | English |
|---|---|
| Article number | 127118 |
| Journal | Neurocomputing |
| Volume | 569 |
| DOIs | |
| Publication status | Published - 7 Feb 2024 |
Bibliographical note
Publisher Copyright:© 2023 Elsevier B.V.
Funding
He is currently a Professor and serves as the Head of the Laboratory for Intelligent Information Processing with the College of Science, China University of Petroleum (East China). His research interests include computational intelligence, machine learning, pattern recognition, deep learning, differential programming, clustering, fuzzy systems, evolutionary computation. He was awarded several grants from the National Science Foundation of China, National Key Research and Development Program of China, Natural Science Foundation of Shandong Province, Fundamental Research Funds for the Central Universities. This work was supported in part by the National Natural Science Foundation of China under Grant 62173345 and Grant 62176040 ; in part by the National Key Research and Development Program of China under Grant 2019YFA0708700 ; in part by the Fundamental Research Funds for the Central Universities under Grant 22CX03002A ; in part by the China-CEEC Higher Education Institutions Consortium Program under Grant 2022151 ; in part by the Introduction Plan for High Talent Foreign Experts under Grant G2023152012L ; in part by the ‘ ‘The Belt and Road” Innovative Talents Exchange Foreign Experts Project under Grant DL2023152001L .
| Funders | Funder number |
|---|---|
| Belt and Road” Innovative Talents Exchange Foreign Experts Project | DL2023152001L |
| China-CEEC Higher Education Institutions Consortium Program | 2022151 |
| Introduction Plan for High Talent Foreign Experts | G2023152012L |
| National Natural Science Foundation of China | 62173345, 62176040 |
| Natural Science Foundation of Shandong Province | |
| National Key Research and Development Program of China | 2019YFA0708700 |
| Fundamental Research Funds for the Central Universities | 22CX03002A |
Keywords
- Adaptive fuzzy partition
- Adaptive membership function
- Mean shift
- Partially combined fuzzy rule base
- Zero-order neuro-fuzzy network