• norsk
    • English
  • English 
    • norsk
    • English
  • Login
View Item 
  •   Home
  • Fakultet for informasjonsteknologi og elektroteknikk (IE)
  • Institutt for datateknologi og informatikk
  • View Item
  •   Home
  • Fakultet for informasjonsteknologi og elektroteknikk (IE)
  • Institutt for datateknologi og informatikk
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Exploring a Developmental Reservoir Computing System Using Self-Modifying Recurrent Cartesian Genetic Programming

Lima, Anders
Master thesis
Thumbnail
View/Open
16097_FULLTEXT.pdf (4.734Mb)
16097_ATTACHMENT.zip (75.73Mb)
16097_COVER.pdf (1.556Mb)
URI
http://hdl.handle.net/11250/2464060
Date
2017
Metadata
Show full item record
Collections
  • Institutt for datateknologi og informatikk [6319]
Abstract
Inspired by biology, numerous new computational models have been proposed as alternatives to cope with the ever-growing complexity of the traditional von Neumann architecture. Vastly parallel systems comprising simple units that only interact locally, form the basis of many of those new systems.

In this thesis, we combine ideas proposed in the field of bio-inspired unconventional architectures. Specifically, we explore the possibility of evolving a developmental reservoir. The reservoir is the heart of a computational model, coined reservoir computing. A reservoir computing system works by perturbing the reservoir with a stream of data. The reservoir will extract high-dimensional features of the data stream, which is classified in a readout layer by a linear classifier. A static network with a random recurrent topology is often used as a reservoir. However, we propose a self-modifying reservoir that is able to develop and adapt to the perturbations, such that the reservoir structure self-organises in a way that enables it to transform the input into a high-dimensional feature set. Additionally, development will enable a large reservoir to be grown from a relatively small genotype.

The system implemented is an extension of a recurrent Cartesian genetic programming reservoir computing system presented in the specialisation project by the author. The extension support the self-modifying operations required in a developmental system.

Fitness functions based on separability and development is used in the endeavour of finding a self-organising computationally capable reservoir. We will explore how the of genotype size, and the environment affect the reservoir. Additionally, the temporal parity problem is solved to demonstrate the system's performance.

The results show that finding a genotype that develops into a reservoir with the aforementioned features is rather difficult. Nevertheless, examples of working genotypes are found, serving as a proof of concept.
Publisher
NTNU

Contact Us | Send Feedback

Privacy policy
DSpace software copyright © 2002-2019  DuraSpace

Service from  Unit
 

 

Browse

ArchiveCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsDocument TypesJournalsThis CollectionBy Issue DateAuthorsTitlesSubjectsDocument TypesJournals

My Account

Login

Statistics

View Usage Statistics

Contact Us | Send Feedback

Privacy policy
DSpace software copyright © 2002-2019  DuraSpace

Service from  Unit