Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/1361
Title: Motion learning using spatio-temporal neural network
Authors: Nooraini Yusoff 
Farzana Kabir-Ahmad 
Mohamad-Farif Jemili 
Keywords: Motion learning;Reinforcement learning;Reward-modulated spike-timing-dependent plasticity;Spatio-temporal neural network
Issue Date: 2020
Publisher: UUM Press
Project: FRGS 
Journal: Journal of Information and Communication Technology 
Abstract: 
Motion trajectory prediction is one of the key areas in behaviour and surveillance studies. Many related successful applications have been reported in the literature. However, most of the studies are based on sigmoidal neural networks in which some dynamic properties of the data are overlooked due to the absence of spatiotemporal encoding functionalities. Even though some sequential (motion) learning studies have been proposed using spatiotemporal neural networks, as in those sigmoidal neural networks, the approach used is mainly supervised learning. In such learning, it requires a target signal, in which this is not always available in some applications. For this study, motion learning using spatiotemporal neural network is proposed. The learning is based on reward-modulated spike-timing-dependent plasticity (STDP), whereby the learning weight adjustment provided by the standard STDP is modulated by the reinforcement. The implementation of reinforcement approach for motion trajectory can be regarded as a major contribution of this study. In this study, learning is implemented on a reward basis without the need for learning targets. The algorithm has shown good potential in learning motion trajectory particularly in noisy and dynamic settings. Furthermore, the learning uses generic neural network architecture, which makes learning adaptable for many applications.
Description: 
Scopus
URI: http://hdl.handle.net/123456789/1361
ISSN: 1978-3086
Appears in Collections:Faculty of Bioengineering and Technology - Journal (Scopus/WOS)

Files in This Item:
File Description SizeFormat
207-223.pdf1.51 MBAdobe PDFView/Open
Show full item record

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.