SoHyFIS-Yager : a self-organizing Yager based hybrid neural fuzzy inference system
The Hybrid neural Fuzzy Inference System (HyFIS) is a multilayer adaptive neural fuzzy system for building and optimizing fuzzy models using neural networks. In this paper, the fuzzy Yager inference scheme, which is able to emulate the human deductive reasoning logic, is integrated into the HyFIS mo...
| Main Authors: | , , |
|---|---|
| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2013
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/97611 http://hdl.handle.net/10220/11128 |
| _version_ | 1824454626092515328 |
|---|---|
| author | Tung, Sau Wai Quek, Chai Guan, Cuntai |
| author2 | School of Computer Engineering |
| author_facet | School of Computer Engineering Tung, Sau Wai Quek, Chai Guan, Cuntai |
| author_sort | Tung, Sau Wai |
| collection | NTU |
| description | The Hybrid neural Fuzzy Inference System (HyFIS) is a multilayer adaptive neural fuzzy system for building and optimizing fuzzy models using neural networks. In this paper, the fuzzy Yager inference scheme, which is able to emulate the human deductive reasoning logic, is integrated into the HyFIS model to provide it with a firm and intuitive logical reasoning and decision-making framework. In addition, a self-organizing gaussian Discrete Incremental Clustering (gDIC) technique is implemented in the network to automatically form fuzzy sets in the fuzzification phase. This clustering technique is no longer limited by the need to have prior knowledge about the number of clusters present in each input and output dimensions. The proposed self-organizing Yager based Hybrid neural Fuzzy Inference System (SoHyFIS-Yager) introduces the learning power of neural networks to fuzzy logic systems, while providing linguistic explanations of the fuzzy logic systems to the connectionist networks. Extensive simulations were conducted using the proposed model and its performance demonstrates its superiority as an effective neuro-fuzzy modeling technique. |
| first_indexed | 2025-02-19T03:25:18Z |
| format | Journal Article |
| id | ntu-10356/97611 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-02-19T03:25:18Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | ntu-10356/976112020-05-28T07:19:12Z SoHyFIS-Yager : a self-organizing Yager based hybrid neural fuzzy inference system Tung, Sau Wai Quek, Chai Guan, Cuntai School of Computer Engineering Centre for Computational Intelligence DRNTU::Engineering::Computer science and engineering::Computer applications The Hybrid neural Fuzzy Inference System (HyFIS) is a multilayer adaptive neural fuzzy system for building and optimizing fuzzy models using neural networks. In this paper, the fuzzy Yager inference scheme, which is able to emulate the human deductive reasoning logic, is integrated into the HyFIS model to provide it with a firm and intuitive logical reasoning and decision-making framework. In addition, a self-organizing gaussian Discrete Incremental Clustering (gDIC) technique is implemented in the network to automatically form fuzzy sets in the fuzzification phase. This clustering technique is no longer limited by the need to have prior knowledge about the number of clusters present in each input and output dimensions. The proposed self-organizing Yager based Hybrid neural Fuzzy Inference System (SoHyFIS-Yager) introduces the learning power of neural networks to fuzzy logic systems, while providing linguistic explanations of the fuzzy logic systems to the connectionist networks. Extensive simulations were conducted using the proposed model and its performance demonstrates its superiority as an effective neuro-fuzzy modeling technique. 2013-07-10T07:50:03Z 2019-12-06T19:44:35Z 2013-07-10T07:50:03Z 2019-12-06T19:44:35Z 2012 2012 Journal Article Tung, S. W., Quek, C., & Guan, C. (2012). SoHyFIS-Yager : a self-organizing Yager based hybrid neural fuzzy inference system. Expert systems with applications, 39(17), 12759-12771. https://hdl.handle.net/10356/97611 http://hdl.handle.net/10220/11128 10.1016/j.eswa.2012.02.056 en Expert systems with applications © 2012 Published by Elsevier Ltd. |
| spellingShingle | DRNTU::Engineering::Computer science and engineering::Computer applications Tung, Sau Wai Quek, Chai Guan, Cuntai SoHyFIS-Yager : a self-organizing Yager based hybrid neural fuzzy inference system |
| title | SoHyFIS-Yager : a self-organizing Yager based hybrid neural fuzzy inference system |
| title_full | SoHyFIS-Yager : a self-organizing Yager based hybrid neural fuzzy inference system |
| title_fullStr | SoHyFIS-Yager : a self-organizing Yager based hybrid neural fuzzy inference system |
| title_full_unstemmed | SoHyFIS-Yager : a self-organizing Yager based hybrid neural fuzzy inference system |
| title_short | SoHyFIS-Yager : a self-organizing Yager based hybrid neural fuzzy inference system |
| title_sort | sohyfis yager a self organizing yager based hybrid neural fuzzy inference system |
| topic | DRNTU::Engineering::Computer science and engineering::Computer applications |
| url | https://hdl.handle.net/10356/97611 http://hdl.handle.net/10220/11128 |
| work_keys_str_mv | AT tungsauwai sohyfisyageraselforganizingyagerbasedhybridneuralfuzzyinferencesystem AT quekchai sohyfisyageraselforganizingyagerbasedhybridneuralfuzzyinferencesystem AT guancuntai sohyfisyageraselforganizingyagerbasedhybridneuralfuzzyinferencesystem |