mdh.sePublikationer
Ändra sökning
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Real-time Process Modelling Based on Big Data Stream Learning
Mälardalens högskola, Akademin för innovation, design och teknik.
2017 (Engelska)Självständigt arbete på avancerad nivå (masterexamen), 80 poäng / 120 hpStudentuppsats (Examensarbete)
Abstract [en]

Most control systems now are assumed to be unchangeable, but this is an ideal situation. In real applications, they are often accompanied with many changes. Some of changes are from environment changes, and some are system requirements. So, the goal of thesis is to model a dynamic adaptive real-time control system process with big data stream. In this way, control system model can adjust itself using example measurements acquired during the operation and give suggestion to next arrival input, which also indicates the accuracy of states under control highly depends on quality of the process model.

 

In this thesis, we choose recurrent neural network to model process because it is a kind of cheap and fast artificial intelligence. In most of existent artificial intelligence, a database is necessity and the bigger the database is, the more accurate result can be. For example, in case-based reasoning, testcase should be compared with all of cases in database, then take the closer one’s result as reference. However, in neural network, it does not need any big database to support and search, and only needs simple calculation instead, because information is all stored in each connection. All small units called neuron are linear combination, but a neural network made up of neurons can perform some complex and non-linear functionalities. For training part, Backpropagation and Kalman filter are used together. Backpropagation is a widely-used and stable optimization algorithm. Kalman filter is new to gradient-based optimization, but it has been proved to converge faster than other traditional first-order-gradient-based algorithms.

 

Several experiments were prepared to compare new and existent algorithms under various circumstances. The first set of experiments are static systems and they are only used to investigate convergence rate and accuracy of different algorithms. The second set of experiments are time-varying systems and the purpose is to take one more attribute, adaptivity, into consideration.

Ort, förlag, år, upplaga, sidor
2017. , s. 41
Nyckelord [en]
control system, real-time process, deep learning, recurrent neural network, Backpropagation through time, Kalman filter
Nationell ämneskategori
Inbäddad systemteknik
Identifikatorer
URN: urn:nbn:se:mdh:diva-35823OAI: oai:DiVA.org:mdh-35823DiVA, id: diva2:1111073
Handledare
Examinatorer
Tillgänglig från: 2017-08-29 Skapad: 2017-06-17 Senast uppdaterad: 2017-08-29Bibliografiskt granskad

Open Access i DiVA

fulltext(2554 kB)291 nedladdningar
Filinformation
Filnamn FULLTEXT01.pdfFilstorlek 2554 kBChecksumma SHA-512
5558faea91e1b235e3b2992a7867474d4d5e4b394593c8c42732f1ee4d11592de38baa1b81b0f14878e91bd7400ea9ee4e95897ec6edbab073b3de67ddb60d80
Typ fulltextMimetyp application/pdf

Av organisationen
Akademin för innovation, design och teknik
Inbäddad systemteknik

Sök vidare utanför DiVA

GoogleGoogle Scholar
Totalt: 291 nedladdningar
Antalet nedladdningar är summan av nedladdningar för alla fulltexter. Det kan inkludera t.ex tidigare versioner som nu inte längre är tillgängliga.

urn-nbn

Altmetricpoäng

urn-nbn
Totalt: 1064 träffar
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf