Optimal parameters of an ELM-based interval type 2 fuzzy logic system: a hybrid learning algorithm
An optimized design of a fuzzy logic system can be regarded as setting of different parameters of the system automatically. For a single parameter, there may exist multiple feasible values. Consequently, with the increase in number of parameters, the complexity of a system increases. Type 2 fuzzy lo...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Published: |
Springer London
2018
|
| Online Access: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84982840988&doi=10.1007%2fs00521-016-2503-5&partnerID=40&md5=55bd02612807e7f4c425f09a105a5d30 http://eprints.utp.edu.my/21816/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| id |
my.utp.eprints.21816 |
|---|---|
| record_format |
eprints |
| spelling |
my.utp.eprints.218162018-08-01T02:02:32Z Optimal parameters of an ELM-based interval type 2 fuzzy logic system: a hybrid learning algorithm Hassan, S. Khanesar, M.A. Jaafar, J. Khosravi, A. An optimized design of a fuzzy logic system can be regarded as setting of different parameters of the system automatically. For a single parameter, there may exist multiple feasible values. Consequently, with the increase in number of parameters, the complexity of a system increases. Type 2 fuzzy logic system has more parameters than the type 1 fuzzy logic system and is therefore much more complex than its counterpart. This paper proposes optimal parameters for an extreme learning machine-based interval type 2 fuzzy logic system to learn its best configuration. Extreme learning machine (ELM) is utilized to tune the consequent parameters of the interval type 2 fuzzy logic system (IT2FLS). A disadvantage of ELM is the random generation of its hidden neuron that causes additional uncertainty, in both approximation and learning. In order to overcome this limitation in an ELM-based IT2FLS, artificial bee colony optimization algorithm is utilized to obtain its antecedent parts parameters. The simulation results verified better performance of the proposed IT2FLS over other models with the benchmark data sets. © 2016, The Natural Computing Applications Forum. Springer London 2018 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-84982840988&doi=10.1007%2fs00521-016-2503-5&partnerID=40&md5=55bd02612807e7f4c425f09a105a5d30 Hassan, S. and Khanesar, M.A. and Jaafar, J. and Khosravi, A. (2018) Optimal parameters of an ELM-based interval type 2 fuzzy logic system: a hybrid learning algorithm. Neural Computing and Applications, 29 (4). pp. 1001-1014. http://eprints.utp.edu.my/21816/ |
| institution |
Universiti Teknologi Petronas |
| building |
UTP Resource Centre |
| collection |
Institutional Repository |
| continent |
Asia |
| country |
Malaysia |
| content_provider |
Universiti Teknologi Petronas |
| content_source |
UTP Institutional Repository |
| url_provider |
http://eprints.utp.edu.my/ |
| description |
An optimized design of a fuzzy logic system can be regarded as setting of different parameters of the system automatically. For a single parameter, there may exist multiple feasible values. Consequently, with the increase in number of parameters, the complexity of a system increases. Type 2 fuzzy logic system has more parameters than the type 1 fuzzy logic system and is therefore much more complex than its counterpart. This paper proposes optimal parameters for an extreme learning machine-based interval type 2 fuzzy logic system to learn its best configuration. Extreme learning machine (ELM) is utilized to tune the consequent parameters of the interval type 2 fuzzy logic system (IT2FLS). A disadvantage of ELM is the random generation of its hidden neuron that causes additional uncertainty, in both approximation and learning. In order to overcome this limitation in an ELM-based IT2FLS, artificial bee colony optimization algorithm is utilized to obtain its antecedent parts parameters. The simulation results verified better performance of the proposed IT2FLS over other models with the benchmark data sets. © 2016, The Natural Computing Applications Forum. |
| format |
Article |
| author |
Hassan, S. Khanesar, M.A. Jaafar, J. Khosravi, A. |
| spellingShingle |
Hassan, S. Khanesar, M.A. Jaafar, J. Khosravi, A. Optimal parameters of an ELM-based interval type 2 fuzzy logic system: a hybrid learning algorithm |
| author_facet |
Hassan, S. Khanesar, M.A. Jaafar, J. Khosravi, A. |
| author_sort |
Hassan, S. |
| title |
Optimal parameters of an ELM-based interval type 2 fuzzy logic system: a hybrid learning algorithm |
| title_short |
Optimal parameters of an ELM-based interval type 2 fuzzy logic system: a hybrid learning algorithm |
| title_full |
Optimal parameters of an ELM-based interval type 2 fuzzy logic system: a hybrid learning algorithm |
| title_fullStr |
Optimal parameters of an ELM-based interval type 2 fuzzy logic system: a hybrid learning algorithm |
| title_full_unstemmed |
Optimal parameters of an ELM-based interval type 2 fuzzy logic system: a hybrid learning algorithm |
| title_sort |
optimal parameters of an elm-based interval type 2 fuzzy logic system: a hybrid learning algorithm |
| publisher |
Springer London |
| publishDate |
2018 |
| url |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84982840988&doi=10.1007%2fs00521-016-2503-5&partnerID=40&md5=55bd02612807e7f4c425f09a105a5d30 http://eprints.utp.edu.my/21816/ |
| _version_ |
1738656342218899456 |
| score |
13.211869 |