A meta-cognitive sequential learning algorithm for neuro-fuzzy inference system
In this paper, we present a meta-cognitive sequential learning algorithm for a neuro-fuzzy inference system, referred to as, ‘Meta-Cognitive Neuro-Fuzzy Inference System’ (McFIS). McFIS has two components, viz., a cognitive component and a meta-cognitive component. The cognitive component employed i...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2013
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| Online Access: | https://hdl.handle.net/10356/99120 http://hdl.handle.net/10220/12556 |
| _version_ | 1811688285658939392 |
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| author | Suresh, Sundaram Subramanian, K. |
| author2 | School of Computer Engineering |
| author_facet | School of Computer Engineering Suresh, Sundaram Subramanian, K. |
| author_sort | Suresh, Sundaram |
| collection | NTU |
| description | In this paper, we present a meta-cognitive sequential learning algorithm for a neuro-fuzzy inference system, referred to as, ‘Meta-Cognitive Neuro-Fuzzy Inference System’ (McFIS). McFIS has two components, viz., a cognitive component and a meta-cognitive component. The cognitive component employed is a Takagi–Sugeno–Kang type-0 neuro-fuzzy inference system. A self-regulatory learning mechanism that controls the learning process of the cognitive component, by deciding what-to-learn, when-to-learn and how-to-learn from sequential training data, forms the meta-cognitive component. McFIS realizes the above decision by employing sample deletion, sample reserve and sample learning strategy, respectively. The meta-cognitive component use the instantaneous error of the sample and spherical potential of the rule antecedents to select the best training strategy for the current sample. Also, in sample learning strategy, when a new rule is added the rule consequent is assigned such that the localization property of Gaussian rule is fully exploited. The performance of McFIS is evaluated on four regression and eight classification problems. The performance comparison shows the superior generalization performance of McFIS compared to existing algorithms. |
| first_indexed | 2024-10-01T05:29:47Z |
| format | Journal Article |
| id | ntu-10356/99120 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:29:47Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | ntu-10356/991202020-05-28T07:18:11Z A meta-cognitive sequential learning algorithm for neuro-fuzzy inference system Suresh, Sundaram Subramanian, K. School of Computer Engineering In this paper, we present a meta-cognitive sequential learning algorithm for a neuro-fuzzy inference system, referred to as, ‘Meta-Cognitive Neuro-Fuzzy Inference System’ (McFIS). McFIS has two components, viz., a cognitive component and a meta-cognitive component. The cognitive component employed is a Takagi–Sugeno–Kang type-0 neuro-fuzzy inference system. A self-regulatory learning mechanism that controls the learning process of the cognitive component, by deciding what-to-learn, when-to-learn and how-to-learn from sequential training data, forms the meta-cognitive component. McFIS realizes the above decision by employing sample deletion, sample reserve and sample learning strategy, respectively. The meta-cognitive component use the instantaneous error of the sample and spherical potential of the rule antecedents to select the best training strategy for the current sample. Also, in sample learning strategy, when a new rule is added the rule consequent is assigned such that the localization property of Gaussian rule is fully exploited. The performance of McFIS is evaluated on four regression and eight classification problems. The performance comparison shows the superior generalization performance of McFIS compared to existing algorithms. 2013-07-31T03:23:15Z 2019-12-06T20:03:36Z 2013-07-31T03:23:15Z 2019-12-06T20:03:36Z 2012 2012 Journal Article Subramanian, K.,& Suresh, S. (2012). A meta-cognitive sequential learning algorithm for neuro-fuzzy inference system. Applied Soft Computing, 12(11), 3603-3614. 1568-4946 https://hdl.handle.net/10356/99120 http://hdl.handle.net/10220/12556 10.1016/j.asoc.2012.06.012 en Applied soft computing |
| spellingShingle | Suresh, Sundaram Subramanian, K. A meta-cognitive sequential learning algorithm for neuro-fuzzy inference system |
| title | A meta-cognitive sequential learning algorithm for neuro-fuzzy inference system |
| title_full | A meta-cognitive sequential learning algorithm for neuro-fuzzy inference system |
| title_fullStr | A meta-cognitive sequential learning algorithm for neuro-fuzzy inference system |
| title_full_unstemmed | A meta-cognitive sequential learning algorithm for neuro-fuzzy inference system |
| title_short | A meta-cognitive sequential learning algorithm for neuro-fuzzy inference system |
| title_sort | meta cognitive sequential learning algorithm for neuro fuzzy inference system |
| url | https://hdl.handle.net/10356/99120 http://hdl.handle.net/10220/12556 |
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