Ontology type: schema:ScholarlyArticle
2017-09-13
AUTHORSE. C. Bezerra, R. P. S. Leão, A. P. de S. Braga
ABSTRACTOperational research has made meaningful contributions to practical forecasting in organizations. An area of substantial activity has been in nonlinear modeling. Based on Particle Swarm Optimization, we discuss a nonlinear method where a self-adaptive approach, named as Particle Swarm Optimization with aging and weakening factors, was applied to training a Focused Time Delay Neural Network. Three freely available benchmark datasets were used to demonstrate the features of the proposed approach compared to the Backpropagation algorithm, Differential Evolution and the Particle Swarm Optimization method when applied for training the artificial neural network. Even acknowledging that the effort in comparing methods across multiple empirical datasets is certainly substantial, the proposed algorithm was used to produce 30 min, 1, 3 and 6 h ahead predictions of wind speed at one site in Brazil. The use of the proposed algorithm goes further than only training the artificial neural network, but also searching the best number of hidden neurons and number of lags. The results have shown that the modified Particle Swarm Optimization algorithm obtained better results in all predictions horizons, and the use of it has remarkably reduced the training time. More... »
PAGES785-795
http://scigraph.springernature.com/pub.10.1007/s40313-017-0339-6
DOIhttp://dx.doi.org/10.1007/s40313-017-0339-6
DIMENSIONShttps://app.dimensions.ai/details/publication/pub.1091595507
JSON-LD is the canonical representation for SciGraph data.
TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT
[
{
"@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json",
"about": [
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Information and Computing Sciences",
"type": "DefinedTerm"
},
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0801",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Artificial Intelligence and Image Processing",
"type": "DefinedTerm"
}
],
"author": [
{
"affiliation": {
"alternateName": "Department of Electrical Engineering, Universidade Federal do Cear\u00e1, Campus do Pici, Caixa Postal 6001, 60455-760, Fortaleza, Cear\u00e1, Brazil",
"id": "http://www.grid.ac/institutes/grid.8395.7",
"name": [
"Department of Electrical Engineering, Universidade Federal do Cear\u00e1, Campus do Pici, Caixa Postal 6001, 60455-760, Fortaleza, Cear\u00e1, Brazil"
],
"type": "Organization"
},
"familyName": "Bezerra",
"givenName": "E. C.",
"id": "sg:person.011156015635.55",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011156015635.55"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Department of Electrical Engineering, Universidade Federal do Cear\u00e1, Campus do Pici, Caixa Postal 6001, 60455-760, Fortaleza, Cear\u00e1, Brazil",
"id": "http://www.grid.ac/institutes/grid.8395.7",
"name": [
"Department of Electrical Engineering, Universidade Federal do Cear\u00e1, Campus do Pici, Caixa Postal 6001, 60455-760, Fortaleza, Cear\u00e1, Brazil"
],
"type": "Organization"
},
"familyName": "Le\u00e3o",
"givenName": "R. P. S.",
"id": "sg:person.014051323062.52",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014051323062.52"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Department of Electrical Engineering, Universidade Federal do Cear\u00e1, Campus do Pici, Caixa Postal 6001, 60455-760, Fortaleza, Cear\u00e1, Brazil",
"id": "http://www.grid.ac/institutes/grid.8395.7",
"name": [
"Department of Electrical Engineering, Universidade Federal do Cear\u00e1, Campus do Pici, Caixa Postal 6001, 60455-760, Fortaleza, Cear\u00e1, Brazil"
],
"type": "Organization"
},
"familyName": "Braga",
"givenName": "A. P. de S.",
"id": "sg:person.013346337235.28",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013346337235.28"
],
"type": "Person"
}
],
"citation": [
{
"id": "sg:pub.10.1038/323533a0",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1018367015",
"https://doi.org/10.1038/323533a0"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1023/a:1008202821328",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1012950914",
"https://doi.org/10.1023/a:1008202821328"
],
"type": "CreativeWork"
}
],
"datePublished": "2017-09-13",
"datePublishedReg": "2017-09-13",
"description": "Operational research has made meaningful contributions to practical forecasting in organizations. An area of substantial activity has been in nonlinear modeling. Based on Particle Swarm Optimization, we discuss a nonlinear method where a self-adaptive approach, named as Particle Swarm Optimization with aging and weakening factors, was applied to training a Focused Time Delay Neural Network. Three freely available benchmark datasets were used to demonstrate the features of the proposed approach compared to the Backpropagation algorithm, Differential Evolution and the Particle Swarm Optimization method when applied for training the artificial neural network. Even acknowledging that the effort in comparing methods across multiple empirical datasets is certainly substantial, the proposed algorithm was used to produce 30 min, 1, 3 and 6 h ahead predictions of wind speed at one site in Brazil. The use of the proposed algorithm goes further than only training the artificial neural network, but also searching the best number of hidden neurons and number of lags. The results have shown that the modified Particle Swarm Optimization algorithm obtained better results in all predictions horizons, and the use of it has remarkably reduced the training time.",
"genre": "article",
"id": "sg:pub.10.1007/s40313-017-0339-6",
"isAccessibleForFree": false,
"isPartOf": [
{
"id": "sg:journal.1136425",
"issn": [
"0103-1759",
"1807-0345"
],
"name": "Journal of Control, Automation and Electrical Systems",
"publisher": "Springer Nature",
"type": "Periodical"
},
{
"issueNumber": "6",
"type": "PublicationIssue"
},
{
"type": "PublicationVolume",
"volumeNumber": "28"
}
],
"keywords": [
"particle swarm optimization",
"swarm optimization",
"particle swarm optimization method",
"swarm optimization method",
"particle swarm optimization algorithm",
"swarm optimization algorithm",
"prediction horizon",
"self-adaptive approach",
"differential evolution",
"optimization method",
"nonlinear modeling",
"optimization algorithm",
"nonlinear methods",
"neural network",
"number of lags",
"artificial neural network",
"delay neural network",
"time-delay neural network",
"wind speed forecasting",
"optimization",
"empirical datasets",
"algorithm",
"backpropagation algorithm",
"speed forecasting",
"Focused Time Delay Neural Network",
"operational research",
"practical forecasting",
"benchmark datasets",
"network",
"available benchmark datasets",
"wind speed",
"training time",
"forecasting",
"approach",
"good number",
"modeling",
"better results",
"number",
"horizon",
"prediction",
"speed",
"dataset",
"results",
"evolution",
"lag",
"contribution",
"features",
"time",
"use",
"efforts",
"meaningful contribution",
"area",
"research",
"weakening factor",
"factors",
"sites",
"neurons",
"organization",
"method",
"min",
"activity",
"Brazil",
"substantial activity"
],
"name": "A Self-Adaptive Approach for Particle Swarm Optimization Applied to Wind Speed Forecasting",
"pagination": "785-795",
"productId": [
{
"name": "dimensions_id",
"type": "PropertyValue",
"value": [
"pub.1091595507"
]
},
{
"name": "doi",
"type": "PropertyValue",
"value": [
"10.1007/s40313-017-0339-6"
]
}
],
"sameAs": [
"https://doi.org/10.1007/s40313-017-0339-6",
"https://app.dimensions.ai/details/publication/pub.1091595507"
],
"sdDataset": "articles",
"sdDatePublished": "2022-08-04T17:06",
"sdLicense": "https://scigraph.springernature.com/explorer/license/",
"sdPublisher": {
"name": "Springer Nature - SN SciGraph project",
"type": "Organization"
},
"sdSource": "s3://com-springernature-scigraph/baseset/20220804/entities/gbq_results/article/article_743.jsonl",
"type": "ScholarlyArticle",
"url": "https://doi.org/10.1007/s40313-017-0339-6"
}
]
Download the RDF metadata as: json-ld nt turtle xml License info
JSON-LD is a popular format for linked data which is fully compatible with JSON.
curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s40313-017-0339-6'
N-Triples is a line-based linked data format ideal for batch operations.
curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s40313-017-0339-6'
Turtle is a human-readable linked data format.
curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s40313-017-0339-6'
RDF/XML is a standard XML format for linked data.
curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s40313-017-0339-6'
This table displays all metadata directly associated to this object as RDF triples.
142 TRIPLES
21 PREDICATES
89 URIs
79 LITERALS
6 BLANK NODES
Subject | Predicate | Object | |
---|---|---|---|
1 | sg:pub.10.1007/s40313-017-0339-6 | schema:about | anzsrc-for:08 |
2 | ″ | ″ | anzsrc-for:0801 |
3 | ″ | schema:author | N37651f21c65f498f8b73b7968972d1f2 |
4 | ″ | schema:citation | sg:pub.10.1023/a:1008202821328 |
5 | ″ | ″ | sg:pub.10.1038/323533a0 |
6 | ″ | schema:datePublished | 2017-09-13 |
7 | ″ | schema:datePublishedReg | 2017-09-13 |
8 | ″ | schema:description | Operational research has made meaningful contributions to practical forecasting in organizations. An area of substantial activity has been in nonlinear modeling. Based on Particle Swarm Optimization, we discuss a nonlinear method where a self-adaptive approach, named as Particle Swarm Optimization with aging and weakening factors, was applied to training a Focused Time Delay Neural Network. Three freely available benchmark datasets were used to demonstrate the features of the proposed approach compared to the Backpropagation algorithm, Differential Evolution and the Particle Swarm Optimization method when applied for training the artificial neural network. Even acknowledging that the effort in comparing methods across multiple empirical datasets is certainly substantial, the proposed algorithm was used to produce 30 min, 1, 3 and 6 h ahead predictions of wind speed at one site in Brazil. The use of the proposed algorithm goes further than only training the artificial neural network, but also searching the best number of hidden neurons and number of lags. The results have shown that the modified Particle Swarm Optimization algorithm obtained better results in all predictions horizons, and the use of it has remarkably reduced the training time. |
9 | ″ | schema:genre | article |
10 | ″ | schema:isAccessibleForFree | false |
11 | ″ | schema:isPartOf | N64d651fea9ba4139a9b298c2be83ce58 |
12 | ″ | ″ | Nb7f9cae507d64bcdbbfb3cbc35f5769f |
13 | ″ | ″ | sg:journal.1136425 |
14 | ″ | schema:keywords | Brazil |
15 | ″ | ″ | Focused Time Delay Neural Network |
16 | ″ | ″ | activity |
17 | ″ | ″ | algorithm |
18 | ″ | ″ | approach |
19 | ″ | ″ | area |
20 | ″ | ″ | artificial neural network |
21 | ″ | ″ | available benchmark datasets |
22 | ″ | ″ | backpropagation algorithm |
23 | ″ | ″ | benchmark datasets |
24 | ″ | ″ | better results |
25 | ″ | ″ | contribution |
26 | ″ | ″ | dataset |
27 | ″ | ″ | delay neural network |
28 | ″ | ″ | differential evolution |
29 | ″ | ″ | efforts |
30 | ″ | ″ | empirical datasets |
31 | ″ | ″ | evolution |
32 | ″ | ″ | factors |
33 | ″ | ″ | features |
34 | ″ | ″ | forecasting |
35 | ″ | ″ | good number |
36 | ″ | ″ | horizon |
37 | ″ | ″ | lag |
38 | ″ | ″ | meaningful contribution |
39 | ″ | ″ | method |
40 | ″ | ″ | min |
41 | ″ | ″ | modeling |
42 | ″ | ″ | network |
43 | ″ | ″ | neural network |
44 | ″ | ″ | neurons |
45 | ″ | ″ | nonlinear methods |
46 | ″ | ″ | nonlinear modeling |
47 | ″ | ″ | number |
48 | ″ | ″ | number of lags |
49 | ″ | ″ | operational research |
50 | ″ | ″ | optimization |
51 | ″ | ″ | optimization algorithm |
52 | ″ | ″ | optimization method |
53 | ″ | ″ | organization |
54 | ″ | ″ | particle swarm optimization |
55 | ″ | ″ | particle swarm optimization algorithm |
56 | ″ | ″ | particle swarm optimization method |
57 | ″ | ″ | practical forecasting |
58 | ″ | ″ | prediction |
59 | ″ | ″ | prediction horizon |
60 | ″ | ″ | research |
61 | ″ | ″ | results |
62 | ″ | ″ | self-adaptive approach |
63 | ″ | ″ | sites |
64 | ″ | ″ | speed |
65 | ″ | ″ | speed forecasting |
66 | ″ | ″ | substantial activity |
67 | ″ | ″ | swarm optimization |
68 | ″ | ″ | swarm optimization algorithm |
69 | ″ | ″ | swarm optimization method |
70 | ″ | ″ | time |
71 | ″ | ″ | time-delay neural network |
72 | ″ | ″ | training time |
73 | ″ | ″ | use |
74 | ″ | ″ | weakening factor |
75 | ″ | ″ | wind speed |
76 | ″ | ″ | wind speed forecasting |
77 | ″ | schema:name | A Self-Adaptive Approach for Particle Swarm Optimization Applied to Wind Speed Forecasting |
78 | ″ | schema:pagination | 785-795 |
79 | ″ | schema:productId | N972a09012a944874bf1836f99aeac93f |
80 | ″ | ″ | Nae45f83b003640649ef5e90187cbaeab |
81 | ″ | schema:sameAs | https://app.dimensions.ai/details/publication/pub.1091595507 |
82 | ″ | ″ | https://doi.org/10.1007/s40313-017-0339-6 |
83 | ″ | schema:sdDatePublished | 2022-08-04T17:06 |
84 | ″ | schema:sdLicense | https://scigraph.springernature.com/explorer/license/ |
85 | ″ | schema:sdPublisher | N40d717c58f0c49608718fa74382c90ef |
86 | ″ | schema:url | https://doi.org/10.1007/s40313-017-0339-6 |
87 | ″ | sgo:license | sg:explorer/license/ |
88 | ″ | sgo:sdDataset | articles |
89 | ″ | rdf:type | schema:ScholarlyArticle |
90 | N37439624537f46a097fef276d7d16d62 | rdf:first | sg:person.013346337235.28 |
91 | ″ | rdf:rest | rdf:nil |
92 | N37651f21c65f498f8b73b7968972d1f2 | rdf:first | sg:person.011156015635.55 |
93 | ″ | rdf:rest | Ndc02787b29b241f9a789b7b301c21b3a |
94 | N40d717c58f0c49608718fa74382c90ef | schema:name | Springer Nature - SN SciGraph project |
95 | ″ | rdf:type | schema:Organization |
96 | N64d651fea9ba4139a9b298c2be83ce58 | schema:issueNumber | 6 |
97 | ″ | rdf:type | schema:PublicationIssue |
98 | N972a09012a944874bf1836f99aeac93f | schema:name | doi |
99 | ″ | schema:value | 10.1007/s40313-017-0339-6 |
100 | ″ | rdf:type | schema:PropertyValue |
101 | Nae45f83b003640649ef5e90187cbaeab | schema:name | dimensions_id |
102 | ″ | schema:value | pub.1091595507 |
103 | ″ | rdf:type | schema:PropertyValue |
104 | Nb7f9cae507d64bcdbbfb3cbc35f5769f | schema:volumeNumber | 28 |
105 | ″ | rdf:type | schema:PublicationVolume |
106 | Ndc02787b29b241f9a789b7b301c21b3a | rdf:first | sg:person.014051323062.52 |
107 | ″ | rdf:rest | N37439624537f46a097fef276d7d16d62 |
108 | anzsrc-for:08 | schema:inDefinedTermSet | anzsrc-for: |
109 | ″ | schema:name | Information and Computing Sciences |
110 | ″ | rdf:type | schema:DefinedTerm |
111 | anzsrc-for:0801 | schema:inDefinedTermSet | anzsrc-for: |
112 | ″ | schema:name | Artificial Intelligence and Image Processing |
113 | ″ | rdf:type | schema:DefinedTerm |
114 | sg:journal.1136425 | schema:issn | 0103-1759 |
115 | ″ | ″ | 1807-0345 |
116 | ″ | schema:name | Journal of Control, Automation and Electrical Systems |
117 | ″ | schema:publisher | Springer Nature |
118 | ″ | rdf:type | schema:Periodical |
119 | sg:person.011156015635.55 | schema:affiliation | grid-institutes:grid.8395.7 |
120 | ″ | schema:familyName | Bezerra |
121 | ″ | schema:givenName | E. C. |
122 | ″ | schema:sameAs | https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011156015635.55 |
123 | ″ | rdf:type | schema:Person |
124 | sg:person.013346337235.28 | schema:affiliation | grid-institutes:grid.8395.7 |
125 | ″ | schema:familyName | Braga |
126 | ″ | schema:givenName | A. P. de S. |
127 | ″ | schema:sameAs | https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013346337235.28 |
128 | ″ | rdf:type | schema:Person |
129 | sg:person.014051323062.52 | schema:affiliation | grid-institutes:grid.8395.7 |
130 | ″ | schema:familyName | Leão |
131 | ″ | schema:givenName | R. P. S. |
132 | ″ | schema:sameAs | https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014051323062.52 |
133 | ″ | rdf:type | schema:Person |
134 | sg:pub.10.1023/a:1008202821328 | schema:sameAs | https://app.dimensions.ai/details/publication/pub.1012950914 |
135 | ″ | ″ | https://doi.org/10.1023/a:1008202821328 |
136 | ″ | rdf:type | schema:CreativeWork |
137 | sg:pub.10.1038/323533a0 | schema:sameAs | https://app.dimensions.ai/details/publication/pub.1018367015 |
138 | ″ | ″ | https://doi.org/10.1038/323533a0 |
139 | ″ | rdf:type | schema:CreativeWork |
140 | grid-institutes:grid.8395.7 | schema:alternateName | Department of Electrical Engineering, Universidade Federal do Ceará, Campus do Pici, Caixa Postal 6001, 60455-760, Fortaleza, Ceará, Brazil |
141 | ″ | schema:name | Department of Electrical Engineering, Universidade Federal do Ceará, Campus do Pici, Caixa Postal 6001, 60455-760, Fortaleza, Ceará, Brazil |
142 | ″ | rdf:type | schema:Organization |