Please use this identifier to cite or link to this item:
|Title:||Perception and prediction - A connectionist model|
|Source:||Iyer, L.R.,Ho, S.-B. (2013). Perception and prediction - A connectionist model. Proceedings of the 2013 IEEE Symposium on Computational Intelligence for Human-Like Intelligence, CIHLI 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013 : 25-32. ScholarBank@NUS Repository. https://doi.org/10.1109/CIHLI.2013.6613261|
|Abstract:||Generating appropriate responses to incoming stimuli is a fundamental task of an organism. However, in order to generate intelligent responses, it is important to have a deeper understanding of the environment, and make predictions based on this knowledge. Although the ability to make predictions is intrinsic in humans and many animals, it is still a difficult task for a machine with no in built knowledge about the situation. In this paper we present a biologically inspired neural network model that predicts the future trajectory of a moving object after observing its current trajectory. © 2013 IEEE.|
|Source Title:||Proceedings of the 2013 IEEE Symposium on Computational Intelligence for Human-Like Intelligence, CIHLI 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Mar 8, 2018
checked on Mar 10, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.