Webbläsaren som du använder stöds inte av denna webbplats. Alla versioner av Internet Explorer stöds inte längre, av oss eller Microsoft (läs mer här: * https://www.microsoft.com/en-us/microsoft-365/windows/end-of-ie-support).

Var god och använd en modern webbläsare för att ta del av denna webbplats, som t.ex. nyaste versioner av Edge, Chrome, Firefox eller Safari osv.

Classification of System Dynamics Using Neural Networks

In English

Författare

Summary, in English

The thesis is a part of a project which aims at developing highly autonomous controllers. The task of rough classification of system responses is considered. Neutral networks are trained to classify system dynamics from step- and inpulse responses. Two types of networks are discussed, the backpropagation net and Kohonen's self-organizing feature map. The theory behind these algorithms are presented. A method for normalization of the inputs in time and space is given. This is essential for robust classification. Different net configuration, training methods, and noise sensitivity are investigated. It is shown that the networks can be used as classifiers. Rules of thumb for choosing net structure and parameters are given.

Publiceringsår

1993

Språk

Engelska

Publikation/Tidskrift/Serie

MSc Theses

Dokumenttyp

Examensarbete för Yrkesexamen (Avancerad nivå)

Ämne

  • Technology and Engineering

Nyckelord

  • Autonomous controller
  • Neutral networks
  • System dynamics
  • Classification
  • Transient responses
  • Backpropagation
  • Kohonen

Report number

TFRT-5476

Handledare

  • N/A

ISBN/ISSN/Övrigt

  • ISSN: 0280-5316