Neural network based expectation learning in perception control: learning and control with unreliable sensory system View Full Text


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Article Info

DATE

2004-12

AUTHORS

Sherwin A. Guirnaldo, Keigo Watanabe, Kiyotaka Izumi, Kazuo Kiguchi

ABSTRACT

In this article, we investigate the viability of our proposed neural network-based extension of the “perception” control concept introduced by Randløv and Alstrøm. In their work, each of the expectation elements is linearly acquired such that the expectation gives only the dominant information of the recent past. This handicap could become a serious problem when the perception process is applied to real physical systems. Such an approach has no capability to sense the trend or the dynamics in the information. Here, we introduce an extension of the perception control process by using a radial basis function feedforward neural network to learn the trend and the dynamics in the information queue. Through our simulations, we show that our neural network-based method is better than the conventional method. More... »

PAGES

147-152

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10015-004-0302-z

DOI

http://dx.doi.org/10.1007/s10015-004-0302-z

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1004010348


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