NDLI: Dynamic memory by recurrent neural network and its learning by genetic algorithm

Content Provider	IEEE Xplore Digital Library
Author	Fukuda, T. Kohno, T. Shibata, T.
Copyright Year	1993
Description	Author affiliation: Dept. of Mechano-Inform. & Syst., Nagoya Univ., Japan (Fukuda, T.; Kohno, T.)
Abstract	Recurrent neural networks have dynamic characteristics and can express functions of time. The recurrent neural networks can be applied to memorize robotic motions, i.e. trajectory of a manipulator. For this purpose, it is necessary to determine appropriate interconnection weights of the network. Formerly, learning algorithms based on gradient search techniques have been shown. However, it is difficult for the recurrent neural network to learn such functions while using previous approaches because of much computing requirement and limitation of memory. This paper presents a new learning scheme for the recurrent neural networks by genetic algorithm (GA). The GA is applied to determine interconnection weights of the recurrent neural networks. The GA approach is compared with the backpropagation through time which is a famous learning algorithm for the recurrent neural networks. Simulations illustrate the performance of the proposed approach.<>
Starting Page	2815
Ending Page	2820
File Size	559277
Page Count	6
File Format	PDF
ISBN	0780312988
DOI	10.1109/CDC.1993.325709
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1993-12-15
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Recurrent neural networks Genetic algorithms Neural networks Multi-layer neural network Neurons Feedforward neural networks Cost function Feedforward systems Robots Mechanical engineering
Content Type	Text
Resource Type	Article

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