Supervised pseudo self-evolving cerebellar algorithm for generating fuzzy membership functions
The proper generation of fuzzy membership function is of fundamental importance in fuzzy applications. The effectiveness of the membership functions in pattern classifications can be objectively measured in terms of interpretability and classification accuracy in the conformity of the decision bound...
| Main Authors: | , |
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| Format: | Journal Article |
| Language: | English |
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2013
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| Online Access: | https://hdl.handle.net/10356/96048 http://hdl.handle.net/10220/11135 |
| _version_ | 1824454272336527360 |
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| author | Ang, K. K. Quek, Chai |
| author2 | School of Computer Engineering |
| author_facet | School of Computer Engineering Ang, K. K. Quek, Chai |
| author_sort | Ang, K. K. |
| collection | NTU |
| description | The proper generation of fuzzy membership function is of fundamental importance in fuzzy applications. The effectiveness of the membership functions in pattern classifications can be objectively measured in terms of interpretability and classification accuracy in the conformity of the decision boundaries to the inherent probabilistic decision boundaries of the training data. This paper presents the Supervised Pseudo Self-Evolving Cerebellar (SPSEC) algorithm that is bio-inspired from the two-stage development process of the human nervous system whereby the basic architecture are first laid out without any activity-dependent processes and then refined in activity-dependent ways. SPSEC first constructs a cerebellar-like structure in which neurons with high trophic factors evolves to form membership functions that relate intimately to the probability distributions of the data and concomitantly reconcile with defined semantic properties of linguistic variables. The experimental result of using SPSEC to generate fuzzy membership functions is reported and compared with a selection of algorithms using a publicly available UCI Sonar dataset to illustrate its effectiveness. |
| first_indexed | 2025-02-19T03:19:40Z |
| format | Journal Article |
| id | ntu-10356/96048 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-02-19T03:19:40Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | ntu-10356/960482020-05-28T07:19:16Z Supervised pseudo self-evolving cerebellar algorithm for generating fuzzy membership functions Ang, K. K. Quek, Chai School of Computer Engineering Centre for Computational Intelligence DRNTU::Engineering::Computer science and engineering The proper generation of fuzzy membership function is of fundamental importance in fuzzy applications. The effectiveness of the membership functions in pattern classifications can be objectively measured in terms of interpretability and classification accuracy in the conformity of the decision boundaries to the inherent probabilistic decision boundaries of the training data. This paper presents the Supervised Pseudo Self-Evolving Cerebellar (SPSEC) algorithm that is bio-inspired from the two-stage development process of the human nervous system whereby the basic architecture are first laid out without any activity-dependent processes and then refined in activity-dependent ways. SPSEC first constructs a cerebellar-like structure in which neurons with high trophic factors evolves to form membership functions that relate intimately to the probability distributions of the data and concomitantly reconcile with defined semantic properties of linguistic variables. The experimental result of using SPSEC to generate fuzzy membership functions is reported and compared with a selection of algorithms using a publicly available UCI Sonar dataset to illustrate its effectiveness. 2013-07-10T08:20:20Z 2019-12-06T19:24:52Z 2013-07-10T08:20:20Z 2019-12-06T19:24:52Z 2011 2011 Journal Article https://hdl.handle.net/10356/96048 http://hdl.handle.net/10220/11135 10.1016/j.eswa.2011.08.001 en Expert systems with applications © 2011 Elsevier Ltd. |
| spellingShingle | DRNTU::Engineering::Computer science and engineering Ang, K. K. Quek, Chai Supervised pseudo self-evolving cerebellar algorithm for generating fuzzy membership functions |
| title | Supervised pseudo self-evolving cerebellar algorithm for generating fuzzy membership functions |
| title_full | Supervised pseudo self-evolving cerebellar algorithm for generating fuzzy membership functions |
| title_fullStr | Supervised pseudo self-evolving cerebellar algorithm for generating fuzzy membership functions |
| title_full_unstemmed | Supervised pseudo self-evolving cerebellar algorithm for generating fuzzy membership functions |
| title_short | Supervised pseudo self-evolving cerebellar algorithm for generating fuzzy membership functions |
| title_sort | supervised pseudo self evolving cerebellar algorithm for generating fuzzy membership functions |
| topic | DRNTU::Engineering::Computer science and engineering |
| url | https://hdl.handle.net/10356/96048 http://hdl.handle.net/10220/11135 |
| work_keys_str_mv | AT angkk supervisedpseudoselfevolvingcerebellaralgorithmforgeneratingfuzzymembershipfunctions AT quekchai supervisedpseudoselfevolvingcerebellaralgorithmforgeneratingfuzzymembershipfunctions |