A neural network-based adaptive algorithm on the single EWMA controller View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2004-04

AUTHORS

C.-C. Hsu, C.-T. Su

ABSTRACT

The single EWMA controller has been proven to have excellent performance for small disturbances in the run-to-run process. However, incorrect selection of the EWMA parameter can have the opposite effect on the controlled process output. An adaptive system is necessary to automatically adjust the controller parameters on-line in order to have better performance. In this study, a simple and efficient algorithm based on neural networks (NN) is proposed to minimise the inflation of the output variance on line. The authors have shown that the sequence of EWMA gains, generated by a NN-based adaptive approach, converges close to the optimal controller value under IMA (1, 1), step and trend disturbance models. The paper also shows that the NN-based adaptive EWMA controller has a superior performance than its predecessors. More... »

PAGES

586-593

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00170-003-1776-x

DOI

http://dx.doi.org/10.1007/s00170-003-1776-x

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1030275839


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

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/2008/0801", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Artificial Intelligence and Image Processing", 
        "type": "DefinedTerm"
      }, 
      {
        "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"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "National Chiao Tung University", 
          "id": "https://www.grid.ac/institutes/grid.260539.b", 
          "name": [
            "Department of Industrial Engineering and Management, National Chiao Tung University, Hsinchu, Taiwan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Hsu", 
        "givenName": "C.-C.", 
        "id": "sg:person.010053071075.32", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010053071075.32"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "National Chiao Tung University", 
          "id": "https://www.grid.ac/institutes/grid.260539.b", 
          "name": [
            "Department of Industrial Engineering and Management, National Chiao Tung University, Hsinchu, Taiwan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Su", 
        "givenName": "C.-T.", 
        "id": "sg:person.016137101441.02", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016137101441.02"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1080/07408178808966167", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044941276"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/qre.424", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053058872"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/3476.484201", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061157902"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/3476.622882", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061157949"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/66.350755", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061205983"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/66.827338", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061206364"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/66.827349", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061206369"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1116/1.580545", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062190592"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1198/004017002188618572", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064197447"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1198/004017002317375082", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064197464"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1214/ss/1177010638", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064409607"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2307/1270028", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1069421408"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/00224065.2001.11980064", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1101131405"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/00224065.2002.11980162", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1101131503"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/00224065.1993.11979473", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1101149880"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/00224065.1995.11979579", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1101149986"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/00224065.1997.11979749", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1101150156"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2004-04", 
    "datePublishedReg": "2004-04-01", 
    "description": "The single EWMA controller has been proven to have excellent performance for small disturbances in the run-to-run process. However, incorrect selection of the EWMA parameter can have the opposite effect on the controlled process output. An adaptive system is necessary to automatically adjust the controller parameters on-line in order to have better performance. In this study, a simple and efficient algorithm based on neural networks (NN) is proposed to minimise the inflation of the output variance on line. The authors have shown that the sequence of EWMA gains, generated by a NN-based adaptive approach, converges close to the optimal controller value under IMA (1, 1), step and trend disturbance models. The paper also shows that the NN-based adaptive EWMA controller has a superior performance than its predecessors.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s00170-003-1776-x", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1043671", 
        "issn": [
          "0268-3768", 
          "1433-3015"
        ], 
        "name": "The International Journal of Advanced Manufacturing Technology", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "7-8", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "23"
      }
    ], 
    "name": "A neural network-based adaptive algorithm on the single EWMA controller", 
    "pagination": "586-593", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "903aa37e0c4af952c23526e91c224429a9b2510b58d5742c3e2ef20d61a43d58"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s00170-003-1776-x"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1030275839"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s00170-003-1776-x", 
      "https://app.dimensions.ai/details/publication/pub.1030275839"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-10T15:46", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000001_0000000264/records_8664_00000489.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1007/s00170-003-1776-x"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

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/s00170-003-1776-x'

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/s00170-003-1776-x'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s00170-003-1776-x'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s00170-003-1776-x'


 

This table displays all metadata directly associated to this object as RDF triples.

119 TRIPLES      21 PREDICATES      44 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s00170-003-1776-x schema:about anzsrc-for:08
2 anzsrc-for:0801
3 schema:author N590861658a084529938be16d3c9d4fbc
4 schema:citation https://doi.org/10.1002/qre.424
5 https://doi.org/10.1080/00224065.1993.11979473
6 https://doi.org/10.1080/00224065.1995.11979579
7 https://doi.org/10.1080/00224065.1997.11979749
8 https://doi.org/10.1080/00224065.2001.11980064
9 https://doi.org/10.1080/00224065.2002.11980162
10 https://doi.org/10.1080/07408178808966167
11 https://doi.org/10.1109/3476.484201
12 https://doi.org/10.1109/3476.622882
13 https://doi.org/10.1109/66.350755
14 https://doi.org/10.1109/66.827338
15 https://doi.org/10.1109/66.827349
16 https://doi.org/10.1116/1.580545
17 https://doi.org/10.1198/004017002188618572
18 https://doi.org/10.1198/004017002317375082
19 https://doi.org/10.1214/ss/1177010638
20 https://doi.org/10.2307/1270028
21 schema:datePublished 2004-04
22 schema:datePublishedReg 2004-04-01
23 schema:description The single EWMA controller has been proven to have excellent performance for small disturbances in the run-to-run process. However, incorrect selection of the EWMA parameter can have the opposite effect on the controlled process output. An adaptive system is necessary to automatically adjust the controller parameters on-line in order to have better performance. In this study, a simple and efficient algorithm based on neural networks (NN) is proposed to minimise the inflation of the output variance on line. The authors have shown that the sequence of EWMA gains, generated by a NN-based adaptive approach, converges close to the optimal controller value under IMA (1, 1), step and trend disturbance models. The paper also shows that the NN-based adaptive EWMA controller has a superior performance than its predecessors.
24 schema:genre research_article
25 schema:inLanguage en
26 schema:isAccessibleForFree false
27 schema:isPartOf N1d6176b25297406fbf9740f39dc0560f
28 N32eb5091a34a4071867f63e95cd793b6
29 sg:journal.1043671
30 schema:name A neural network-based adaptive algorithm on the single EWMA controller
31 schema:pagination 586-593
32 schema:productId N3b8d92a582c641119aca12e3e3b69c2f
33 N4653126565f84ae0a82ac3a37d40e362
34 N6e13e7de185f4804b331f0107ea7d305
35 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030275839
36 https://doi.org/10.1007/s00170-003-1776-x
37 schema:sdDatePublished 2019-04-10T15:46
38 schema:sdLicense https://scigraph.springernature.com/explorer/license/
39 schema:sdPublisher N85e591809d4a4475bb8f2e9c1361611b
40 schema:url http://link.springer.com/10.1007/s00170-003-1776-x
41 sgo:license sg:explorer/license/
42 sgo:sdDataset articles
43 rdf:type schema:ScholarlyArticle
44 N1d6176b25297406fbf9740f39dc0560f schema:volumeNumber 23
45 rdf:type schema:PublicationVolume
46 N32eb5091a34a4071867f63e95cd793b6 schema:issueNumber 7-8
47 rdf:type schema:PublicationIssue
48 N3b8d92a582c641119aca12e3e3b69c2f schema:name dimensions_id
49 schema:value pub.1030275839
50 rdf:type schema:PropertyValue
51 N4653126565f84ae0a82ac3a37d40e362 schema:name readcube_id
52 schema:value 903aa37e0c4af952c23526e91c224429a9b2510b58d5742c3e2ef20d61a43d58
53 rdf:type schema:PropertyValue
54 N54107f5c633f46cea0872565121c6f0f rdf:first sg:person.016137101441.02
55 rdf:rest rdf:nil
56 N590861658a084529938be16d3c9d4fbc rdf:first sg:person.010053071075.32
57 rdf:rest N54107f5c633f46cea0872565121c6f0f
58 N6e13e7de185f4804b331f0107ea7d305 schema:name doi
59 schema:value 10.1007/s00170-003-1776-x
60 rdf:type schema:PropertyValue
61 N85e591809d4a4475bb8f2e9c1361611b schema:name Springer Nature - SN SciGraph project
62 rdf:type schema:Organization
63 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
64 schema:name Information and Computing Sciences
65 rdf:type schema:DefinedTerm
66 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
67 schema:name Artificial Intelligence and Image Processing
68 rdf:type schema:DefinedTerm
69 sg:journal.1043671 schema:issn 0268-3768
70 1433-3015
71 schema:name The International Journal of Advanced Manufacturing Technology
72 rdf:type schema:Periodical
73 sg:person.010053071075.32 schema:affiliation https://www.grid.ac/institutes/grid.260539.b
74 schema:familyName Hsu
75 schema:givenName C.-C.
76 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010053071075.32
77 rdf:type schema:Person
78 sg:person.016137101441.02 schema:affiliation https://www.grid.ac/institutes/grid.260539.b
79 schema:familyName Su
80 schema:givenName C.-T.
81 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016137101441.02
82 rdf:type schema:Person
83 https://doi.org/10.1002/qre.424 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053058872
84 rdf:type schema:CreativeWork
85 https://doi.org/10.1080/00224065.1993.11979473 schema:sameAs https://app.dimensions.ai/details/publication/pub.1101149880
86 rdf:type schema:CreativeWork
87 https://doi.org/10.1080/00224065.1995.11979579 schema:sameAs https://app.dimensions.ai/details/publication/pub.1101149986
88 rdf:type schema:CreativeWork
89 https://doi.org/10.1080/00224065.1997.11979749 schema:sameAs https://app.dimensions.ai/details/publication/pub.1101150156
90 rdf:type schema:CreativeWork
91 https://doi.org/10.1080/00224065.2001.11980064 schema:sameAs https://app.dimensions.ai/details/publication/pub.1101131405
92 rdf:type schema:CreativeWork
93 https://doi.org/10.1080/00224065.2002.11980162 schema:sameAs https://app.dimensions.ai/details/publication/pub.1101131503
94 rdf:type schema:CreativeWork
95 https://doi.org/10.1080/07408178808966167 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044941276
96 rdf:type schema:CreativeWork
97 https://doi.org/10.1109/3476.484201 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061157902
98 rdf:type schema:CreativeWork
99 https://doi.org/10.1109/3476.622882 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061157949
100 rdf:type schema:CreativeWork
101 https://doi.org/10.1109/66.350755 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061205983
102 rdf:type schema:CreativeWork
103 https://doi.org/10.1109/66.827338 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061206364
104 rdf:type schema:CreativeWork
105 https://doi.org/10.1109/66.827349 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061206369
106 rdf:type schema:CreativeWork
107 https://doi.org/10.1116/1.580545 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062190592
108 rdf:type schema:CreativeWork
109 https://doi.org/10.1198/004017002188618572 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064197447
110 rdf:type schema:CreativeWork
111 https://doi.org/10.1198/004017002317375082 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064197464
112 rdf:type schema:CreativeWork
113 https://doi.org/10.1214/ss/1177010638 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064409607
114 rdf:type schema:CreativeWork
115 https://doi.org/10.2307/1270028 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069421408
116 rdf:type schema:CreativeWork
117 https://www.grid.ac/institutes/grid.260539.b schema:alternateName National Chiao Tung University
118 schema:name Department of Industrial Engineering and Management, National Chiao Tung University, Hsinchu, Taiwan
119 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...