mdh.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Real-time Process Modelling Based on Big Data Stream Learning
Mälardalen University, School of Innovation, Design and Engineering.
2017 (English)Independent thesis Advanced level (degree of Master (Two Years)), 80 credits / 120 HE creditsStudent thesis
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.

Place, publisher, year, edition, pages
2017. , p. 41
Keyword [en]
control system, real-time process, deep learning, recurrent neural network, Backpropagation through time, Kalman filter
National Category
Embedded Systems
Identifiers
URN: urn:nbn:se:mdh:diva-35823OAI: oai:DiVA.org:mdh-35823DiVA, id: diva2:1111073
Supervisors
Examiners
Available from: 2017-08-29 Created: 2017-06-17 Last updated: 2017-08-29Bibliographically approved

Open Access in DiVA

fulltext(2554 kB)96 downloads
File information
File name FULLTEXT01.pdfFile size 2554 kBChecksum SHA-512
5558faea91e1b235e3b2992a7867474d4d5e4b394593c8c42732f1ee4d11592de38baa1b81b0f14878e91bd7400ea9ee4e95897ec6edbab073b3de67ddb60d80
Type fulltextMimetype application/pdf

By organisation
School of Innovation, Design and Engineering
Embedded Systems

Search outside of DiVA

GoogleGoogle Scholar
Total: 96 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 931 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf