A Bayesian Approach with Type-2 Student-tMembership Function for T-S Model Identification Article Swipe
Vikas Singh
,
Homanga Bharadhwaj
,
Nishchal K. Verma
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2009.00822
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2009.00822
Clustering techniques have been proved highly suc-cessful for Takagi-Sugeno (T-S) fuzzy model identification. Inparticular, fuzzyc-regression clustering based on type-2 fuzzyset has been shown the remarkable results on non-sparse databut their performance degraded on sparse data. In this paper, aninnovative architecture for fuzzyc-regression model is presentedand a novel student-tdistribution based membership functionis designed for sparse data modelling. To avoid the overfitting,we have adopted a Bayesian approach for incorporating aGaussian prior on the regression coefficients. Additional noveltyof our approach lies in type-reduction where the final output iscomputed using Karnik Mendel algorithm and the consequentparameters of the model are optimized using Stochastic GradientDescent method. As detailed experimentation, the result showsthat proposed approach outperforms on standard datasets incomparison of various state-of-the-art methods.
Related Topics
Concepts
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2009.00822
- https://arxiv.org/pdf/2009.00822
- OA Status
- green
- References
- 3
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3081824309
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3081824309Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2009.00822Digital Object Identifier
- Title
-
A Bayesian Approach with Type-2 Student-tMembership Function for T-S Model IdentificationWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-09-02Full publication date if available
- Authors
-
Vikas Singh, Homanga Bharadhwaj, Nishchal K. VermaList of authors in order
- Landing page
-
https://arxiv.org/abs/2009.00822Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2009.00822Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2009.00822Direct OA link when available
- Concepts
-
Overfitting, Computer science, Cluster analysis, Bayesian probability, Identification (biology), Artificial intelligence, Fuzzy logic, Data mining, Pattern recognition (psychology), Machine learning, Artificial neural network, Botany, BiologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
3Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W3081824309 |
|---|---|
| doi | https://doi.org/10.48550/arxiv.2009.00822 |
| ids.doi | https://doi.org/10.48550/arxiv.2009.00822 |
| ids.mag | 3081824309 |
| ids.openalex | https://openalex.org/W3081824309 |
| fwci | |
| type | preprint |
| title | A Bayesian Approach with Type-2 Student-tMembership Function for T-S Model Identification |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T10820 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.9995999932289124 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1702 |
| topics[0].subfield.display_name | Artificial Intelligence |
| topics[0].display_name | Fuzzy Logic and Control Systems |
| topics[1].id | https://openalex.org/T10320 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.9976000189781189 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1702 |
| topics[1].subfield.display_name | Artificial Intelligence |
| topics[1].display_name | Neural Networks and Applications |
| topics[2].id | https://openalex.org/T10057 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.9800000190734863 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1707 |
| topics[2].subfield.display_name | Computer Vision and Pattern Recognition |
| topics[2].display_name | Face and Expression Recognition |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C22019652 |
| concepts[0].level | 3 |
| concepts[0].score | 0.8318219184875488 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q331309 |
| concepts[0].display_name | Overfitting |
| concepts[1].id | https://openalex.org/C41008148 |
| concepts[1].level | 0 |
| concepts[1].score | 0.5877670049667358 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[1].display_name | Computer science |
| concepts[2].id | https://openalex.org/C73555534 |
| concepts[2].level | 2 |
| concepts[2].score | 0.5520595908164978 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q622825 |
| concepts[2].display_name | Cluster analysis |
| concepts[3].id | https://openalex.org/C107673813 |
| concepts[3].level | 2 |
| concepts[3].score | 0.5228748321533203 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q812534 |
| concepts[3].display_name | Bayesian probability |
| concepts[4].id | https://openalex.org/C116834253 |
| concepts[4].level | 2 |
| concepts[4].score | 0.4896124005317688 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q2039217 |
| concepts[4].display_name | Identification (biology) |
| concepts[5].id | https://openalex.org/C154945302 |
| concepts[5].level | 1 |
| concepts[5].score | 0.4452958405017853 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[5].display_name | Artificial intelligence |
| concepts[6].id | https://openalex.org/C58166 |
| concepts[6].level | 2 |
| concepts[6].score | 0.4359702467918396 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q224821 |
| concepts[6].display_name | Fuzzy logic |
| concepts[7].id | https://openalex.org/C124101348 |
| concepts[7].level | 1 |
| concepts[7].score | 0.43549588322639465 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q172491 |
| concepts[7].display_name | Data mining |
| concepts[8].id | https://openalex.org/C153180895 |
| concepts[8].level | 2 |
| concepts[8].score | 0.3850986659526825 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q7148389 |
| concepts[8].display_name | Pattern recognition (psychology) |
| concepts[9].id | https://openalex.org/C119857082 |
| concepts[9].level | 1 |
| concepts[9].score | 0.3757480978965759 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[9].display_name | Machine learning |
| concepts[10].id | https://openalex.org/C50644808 |
| concepts[10].level | 2 |
| concepts[10].score | 0.06560632586479187 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q192776 |
| concepts[10].display_name | Artificial neural network |
| concepts[11].id | https://openalex.org/C59822182 |
| concepts[11].level | 1 |
| concepts[11].score | 0.0 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q441 |
| concepts[11].display_name | Botany |
| concepts[12].id | https://openalex.org/C86803240 |
| concepts[12].level | 0 |
| concepts[12].score | 0.0 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q420 |
| concepts[12].display_name | Biology |
| keywords[0].id | https://openalex.org/keywords/overfitting |
| keywords[0].score | 0.8318219184875488 |
| keywords[0].display_name | Overfitting |
| keywords[1].id | https://openalex.org/keywords/computer-science |
| keywords[1].score | 0.5877670049667358 |
| keywords[1].display_name | Computer science |
| keywords[2].id | https://openalex.org/keywords/cluster-analysis |
| keywords[2].score | 0.5520595908164978 |
| keywords[2].display_name | Cluster analysis |
| keywords[3].id | https://openalex.org/keywords/bayesian-probability |
| keywords[3].score | 0.5228748321533203 |
| keywords[3].display_name | Bayesian probability |
| keywords[4].id | https://openalex.org/keywords/identification |
| keywords[4].score | 0.4896124005317688 |
| keywords[4].display_name | Identification (biology) |
| keywords[5].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[5].score | 0.4452958405017853 |
| keywords[5].display_name | Artificial intelligence |
| keywords[6].id | https://openalex.org/keywords/fuzzy-logic |
| keywords[6].score | 0.4359702467918396 |
| keywords[6].display_name | Fuzzy logic |
| keywords[7].id | https://openalex.org/keywords/data-mining |
| keywords[7].score | 0.43549588322639465 |
| keywords[7].display_name | Data mining |
| keywords[8].id | https://openalex.org/keywords/pattern-recognition |
| keywords[8].score | 0.3850986659526825 |
| keywords[8].display_name | Pattern recognition (psychology) |
| keywords[9].id | https://openalex.org/keywords/machine-learning |
| keywords[9].score | 0.3757480978965759 |
| keywords[9].display_name | Machine learning |
| keywords[10].id | https://openalex.org/keywords/artificial-neural-network |
| keywords[10].score | 0.06560632586479187 |
| keywords[10].display_name | Artificial neural network |
| language | en |
| locations[0].id | pmh:oai:arXiv.org:2009.00822 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4306400194 |
| locations[0].source.issn | |
| locations[0].source.type | repository |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | arXiv (Cornell University) |
| locations[0].source.host_organization | https://openalex.org/I205783295 |
| locations[0].source.host_organization_name | Cornell University |
| locations[0].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[0].license | |
| locations[0].pdf_url | https://arxiv.org/pdf/2009.00822 |
| locations[0].version | submittedVersion |
| locations[0].raw_type | |
| locations[0].license_id | |
| locations[0].is_accepted | False |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | http://arxiv.org/abs/2009.00822 |
| locations[1].id | doi:10.48550/arxiv.2009.00822 |
| locations[1].is_oa | True |
| locations[1].source.id | https://openalex.org/S4306400194 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | True |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | arXiv (Cornell University) |
| locations[1].source.host_organization | https://openalex.org/I205783295 |
| locations[1].source.host_organization_name | Cornell University |
| locations[1].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[1].license | cc-by |
| locations[1].pdf_url | |
| locations[1].version | |
| locations[1].raw_type | article |
| locations[1].license_id | https://openalex.org/licenses/cc-by |
| locations[1].is_accepted | False |
| locations[1].is_published | |
| locations[1].raw_source_name | |
| locations[1].landing_page_url | https://doi.org/10.48550/arxiv.2009.00822 |
| indexed_in | arxiv, datacite |
| authorships[0].author.id | https://openalex.org/A5100778999 |
| authorships[0].author.orcid | https://orcid.org/0000-0001-8014-6939 |
| authorships[0].author.display_name | Vikas Singh |
| authorships[0].countries | IN |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I94234084 |
| authorships[0].affiliations[0].raw_affiliation_string | Indian Institute of Technology Kanpur |
| authorships[0].institutions[0].id | https://openalex.org/I94234084 |
| authorships[0].institutions[0].ror | https://ror.org/05pjsgx75 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I94234084 |
| authorships[0].institutions[0].country_code | IN |
| authorships[0].institutions[0].display_name | Indian Institute of Technology Kanpur |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Vikas Singh |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Indian Institute of Technology Kanpur |
| authorships[1].author.id | https://openalex.org/A5064794691 |
| authorships[1].author.orcid | https://orcid.org/0000-0001-5056-8516 |
| authorships[1].author.display_name | Homanga Bharadhwaj |
| authorships[1].countries | IN |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I94234084 |
| authorships[1].affiliations[0].raw_affiliation_string | Indian Institute of Technology Kanpur |
| authorships[1].institutions[0].id | https://openalex.org/I94234084 |
| authorships[1].institutions[0].ror | https://ror.org/05pjsgx75 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I94234084 |
| authorships[1].institutions[0].country_code | IN |
| authorships[1].institutions[0].display_name | Indian Institute of Technology Kanpur |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Homanga Bharadhwaj |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Indian Institute of Technology Kanpur |
| authorships[2].author.id | https://openalex.org/A5014147535 |
| authorships[2].author.orcid | https://orcid.org/0000-0001-8752-5616 |
| authorships[2].author.display_name | Nishchal K. Verma |
| authorships[2].author_position | last |
| authorships[2].raw_author_name | Nishchal K. Verma |
| authorships[2].is_corresponding | False |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://arxiv.org/pdf/2009.00822 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | A Bayesian Approach with Type-2 Student-tMembership Function for T-S Model Identification |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T06:51:31.235846 |
| primary_topic.id | https://openalex.org/T10820 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.9995999932289124 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1702 |
| primary_topic.subfield.display_name | Artificial Intelligence |
| primary_topic.display_name | Fuzzy Logic and Control Systems |
| related_works | https://openalex.org/W4362597605, https://openalex.org/W1574414179, https://openalex.org/W3009056573, https://openalex.org/W4297676672, https://openalex.org/W2922073769, https://openalex.org/W4281702477, https://openalex.org/W2490526372, https://openalex.org/W4376166922, https://openalex.org/W4378510483, https://openalex.org/W4221142204 |
| cited_by_count | 0 |
| locations_count | 2 |
| best_oa_location.id | pmh:oai:arXiv.org:2009.00822 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4306400194 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | arXiv (Cornell University) |
| best_oa_location.source.host_organization | https://openalex.org/I205783295 |
| best_oa_location.source.host_organization_name | Cornell University |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| best_oa_location.license | |
| best_oa_location.pdf_url | https://arxiv.org/pdf/2009.00822 |
| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | http://arxiv.org/abs/2009.00822 |
| primary_location.id | pmh:oai:arXiv.org:2009.00822 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4306400194 |
| primary_location.source.issn | |
| primary_location.source.type | repository |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | arXiv (Cornell University) |
| primary_location.source.host_organization | https://openalex.org/I205783295 |
| primary_location.source.host_organization_name | Cornell University |
| primary_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| primary_location.license | |
| primary_location.pdf_url | https://arxiv.org/pdf/2009.00822 |
| primary_location.version | submittedVersion |
| primary_location.raw_type | |
| primary_location.license_id | |
| primary_location.is_accepted | False |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | http://arxiv.org/abs/2009.00822 |
| publication_date | 2020-09-02 |
| publication_year | 2020 |
| referenced_works | https://openalex.org/W2103535990, https://openalex.org/W2143760316, https://openalex.org/W1999103110 |
| referenced_works_count | 3 |
| abstract_inverted_index.a | 45, 62 |
| abstract_inverted_index.As | 101 |
| abstract_inverted_index.In | 35 |
| abstract_inverted_index.To | 56 |
| abstract_inverted_index.in | 78 |
| abstract_inverted_index.is | 43 |
| abstract_inverted_index.of | 92, 114 |
| abstract_inverted_index.on | 17, 26, 32, 69, 110 |
| abstract_inverted_index.and | 89 |
| abstract_inverted_index.are | 95 |
| abstract_inverted_index.for | 7, 40, 52, 65 |
| abstract_inverted_index.has | 20 |
| abstract_inverted_index.our | 75 |
| abstract_inverted_index.the | 23, 58, 70, 81, 90, 93, 104 |
| abstract_inverted_index.been | 3, 21 |
| abstract_inverted_index.data | 54 |
| abstract_inverted_index.have | 2, 60 |
| abstract_inverted_index.lies | 77 |
| abstract_inverted_index.this | 36 |
| abstract_inverted_index.(T-S) | 9 |
| abstract_inverted_index.avoid | 57 |
| abstract_inverted_index.based | 16, 48 |
| abstract_inverted_index.data. | 34 |
| abstract_inverted_index.final | 82 |
| abstract_inverted_index.fuzzy | 10 |
| abstract_inverted_index.model | 11, 42, 94 |
| abstract_inverted_index.novel | 46 |
| abstract_inverted_index.prior | 68 |
| abstract_inverted_index.shown | 22 |
| abstract_inverted_index.their | 29 |
| abstract_inverted_index.using | 85, 97 |
| abstract_inverted_index.where | 80 |
| abstract_inverted_index.Karnik | 86 |
| abstract_inverted_index.Mendel | 87 |
| abstract_inverted_index.highly | 5 |
| abstract_inverted_index.output | 83 |
| abstract_inverted_index.paper, | 37 |
| abstract_inverted_index.proved | 4 |
| abstract_inverted_index.result | 105 |
| abstract_inverted_index.sparse | 33, 53 |
| abstract_inverted_index.type-2 | 18 |
| abstract_inverted_index.adopted | 61 |
| abstract_inverted_index.databut | 28 |
| abstract_inverted_index.method. | 100 |
| abstract_inverted_index.results | 25 |
| abstract_inverted_index.various | 115 |
| abstract_inverted_index.Bayesian | 63 |
| abstract_inverted_index.approach | 64, 76, 108 |
| abstract_inverted_index.datasets | 112 |
| abstract_inverted_index.degraded | 31 |
| abstract_inverted_index.designed | 51 |
| abstract_inverted_index.detailed | 102 |
| abstract_inverted_index.fuzzyset | 19 |
| abstract_inverted_index.methods. | 117 |
| abstract_inverted_index.proposed | 107 |
| abstract_inverted_index.standard | 111 |
| abstract_inverted_index.aGaussian | 67 |
| abstract_inverted_index.algorithm | 88 |
| abstract_inverted_index.noveltyof | 74 |
| abstract_inverted_index.optimized | 96 |
| abstract_inverted_index.showsthat | 106 |
| abstract_inverted_index.Additional | 73 |
| abstract_inverted_index.Clustering | 0 |
| abstract_inverted_index.Stochastic | 98 |
| abstract_inverted_index.clustering | 15 |
| abstract_inverted_index.functionis | 50 |
| abstract_inverted_index.iscomputed | 84 |
| abstract_inverted_index.membership | 49 |
| abstract_inverted_index.modelling. | 55 |
| abstract_inverted_index.non-sparse | 27 |
| abstract_inverted_index.regression | 71 |
| abstract_inverted_index.remarkable | 24 |
| abstract_inverted_index.techniques | 1 |
| abstract_inverted_index.outperforms | 109 |
| abstract_inverted_index.performance | 30 |
| abstract_inverted_index.suc-cessful | 6 |
| abstract_inverted_index.aninnovative | 38 |
| abstract_inverted_index.architecture | 39 |
| abstract_inverted_index.incomparison | 113 |
| abstract_inverted_index.presentedand | 44 |
| abstract_inverted_index.Inparticular, | 13 |
| abstract_inverted_index.Takagi-Sugeno | 8 |
| abstract_inverted_index.coefficients. | 72 |
| abstract_inverted_index.incorporating | 66 |
| abstract_inverted_index.overfitting,we | 59 |
| abstract_inverted_index.type-reduction | 79 |
| abstract_inverted_index.GradientDescent | 99 |
| abstract_inverted_index.identification. | 12 |
| abstract_inverted_index.experimentation, | 103 |
| abstract_inverted_index.state-of-the-art | 116 |
| abstract_inverted_index.fuzzyc-regression | 14, 41 |
| abstract_inverted_index.consequentparameters | 91 |
| abstract_inverted_index.student-tdistribution | 47 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile |