DocumentCode
627309
Title
Classification of real time moving object using echo state network
Author
Mitul, Abu Farzan ; Rabin, Md Jubayer Alam ; Rakeeb, Muhammad ; Al Mamun Khan, Abdullah ; Rana, G. M. Sultan Mahmud ; Mollah, AbuShahab ; Rahman, Md Hafizur
fYear
2013
fDate
17-18 May 2013
Firstpage
1
Lastpage
6
Abstract
It is well-known that an artificial agent exhibits deterministic dynamics when it moves in a closed real world environment. It is interesting to determine this dynamics when a real biological being such as fish is kept in a real closed environment and free to move in it. This paper determines some deterministic dynamics of fish motion freely moving in a closed environment. The task is divided into several stages - image capturing, image processing, time series extraction, Chaos analysis and Classification. The classification performance is analyzed with feed forward neural network (FNN), Recurrent Neural network, Fuzzy network, Bagged Regression trees and Echo state network (ESN). Simulation result exhibits that the proposed ESN algorithm outperforms other networks.
Keywords
chaos; feedforward neural nets; image classification; image motion analysis; object detection; recurrent neural nets; regression analysis; time series; trees (mathematics); ESN algorithm; FNN; artificial agent; bagged regression trees; chaos analysis; classification performance analysis; closed-real-world environment; echo state network; feedforward neural network; fish motion deterministic dynamics; fuzzy network; image capturing; image processing; real-time moving object classification; recurrent neural network; time series extraction; Accuracy; Chaos; Correlation; Marine animals; Neural networks; Time series analysis; Training; Echo state network (ESN); Recurrent Neural network; correlation dimension; motion tracking; time series analysis;
fLanguage
English
Publisher
ieee
Conference_Titel
Informatics, Electronics & Vision (ICIEV), 2013 International Conference on
Conference_Location
Dhaka
Print_ISBN
978-1-4799-0397-9
Type
conf
DOI
10.1109/ICIEV.2013.6572662
Filename
6572662
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