In English

Hierarchical Temporal Memory for Behavior Prediction

David Björkman
Göteborg : Chalmers tekniska högskola, 2012. Report - IT University of Göteborg, Chalmers University of Technology and the University of Göteborg , ISSN 1651-4769, 2012.
[Examensarbete på avancerad nivå]

This thesis is about researching and analyzing Hierarchical Temporal Memory, specifically the newly developed "HTM Cortical learning algorithms"[3] developed by Jeff Hawkins and the company Numenta. Two problems are addressed. Can this type of hierarchical memory system make an internal representation of simple data sequences at the input? And if so, does it take long to learn? Two C++ applications were developed in this thesis. The first program is used to analyze the algorithm, and the second program is used to visualize the internal states of the network. The results is very dependent of how the system is configured. If enough resources are available, the system can learn sequences, and it does not take long for the system to learn.

Nyckelord: HTM: Hierarchical Temporal Memory



Publikationen registrerades 2012-09-13. Den ändrades senast 2013-04-04

CPL ID: 163276

Detta är en tjänst från Chalmers bibliotek