Development of parsimonious fuzzy neural network for data driven modelling
In many complex real-world engineering problems, it is well known that the optimal number of rules is unknown. Hence, the fuzzy systems need to acquire knowledge by continuous self-organizing its rules to adapt to changing patterns in evolving data streams. Much-efforts have been devoted by several...
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| Format: | Thesis |
| Language: | English |
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2013
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| Online Access: | http://hdl.handle.net/10356/51025 |
| _version_ | 1811679812689854464 |
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| author | Mahardhika Pratama. |
| author2 | Er Meng Joo |
| author_facet | Er Meng Joo Mahardhika Pratama. |
| author_sort | Mahardhika Pratama. |
| collection | NTU |
| description | In many complex real-world engineering problems, it is well known that the optimal number of rules is unknown. Hence, the fuzzy systems need to acquire knowledge by continuous self-organizing its rules to adapt to changing patterns in evolving data streams. Much-efforts have been devoted by several scientists to develop self-organizing fuzzy systems. Yet, the majority of conventional techniques are mainly concerned with independent issue on structural complexity, prediction accuracy, and structural complexity without taking into account a balance among them. Highly accurate methods usually suffer high computational burden thereby limiting scalability in practical applications. In this dissertation, two novel self-organizing fuzzy systems, which attempts to strike a balance among structural complexity, prediction accuracy, and training speed, are proposed. |
| first_indexed | 2024-10-01T03:15:06Z |
| format | Thesis |
| id | ntu-10356/51025 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:15:06Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | ntu-10356/510252023-07-04T15:37:15Z Development of parsimonious fuzzy neural network for data driven modelling Mahardhika Pratama. Er Meng Joo School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering In many complex real-world engineering problems, it is well known that the optimal number of rules is unknown. Hence, the fuzzy systems need to acquire knowledge by continuous self-organizing its rules to adapt to changing patterns in evolving data streams. Much-efforts have been devoted by several scientists to develop self-organizing fuzzy systems. Yet, the majority of conventional techniques are mainly concerned with independent issue on structural complexity, prediction accuracy, and structural complexity without taking into account a balance among them. Highly accurate methods usually suffer high computational burden thereby limiting scalability in practical applications. In this dissertation, two novel self-organizing fuzzy systems, which attempts to strike a balance among structural complexity, prediction accuracy, and training speed, are proposed. Master of Science (Computer Control and Automation) 2013-01-03T01:58:07Z 2013-01-03T01:58:07Z 2011 2011 Thesis http://hdl.handle.net/10356/51025 en 103 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Electrical and electronic engineering Mahardhika Pratama. Development of parsimonious fuzzy neural network for data driven modelling |
| title | Development of parsimonious fuzzy neural network for data driven modelling |
| title_full | Development of parsimonious fuzzy neural network for data driven modelling |
| title_fullStr | Development of parsimonious fuzzy neural network for data driven modelling |
| title_full_unstemmed | Development of parsimonious fuzzy neural network for data driven modelling |
| title_short | Development of parsimonious fuzzy neural network for data driven modelling |
| title_sort | development of parsimonious fuzzy neural network for data driven modelling |
| topic | DRNTU::Engineering::Electrical and electronic engineering |
| url | http://hdl.handle.net/10356/51025 |
| work_keys_str_mv | AT mahardhikapratama developmentofparsimoniousfuzzyneuralnetworkfordatadrivenmodelling |