Title :
A study on generalization ability of 3-layer recurrent neural networks
Author :
Ninomiya, Hiroshi ; SASAKI, Ayako
Author_Institution :
Dept. of Inf. Sci., Shonan Inst. of Technol., Fujisawa, Japan
fDate :
6/24/1905 12:00:00 AM
Abstract :
In this paper, we report a study on the generalization ability of 3-layer recurrent neural networks (3LRNN). 3LRNN are composed of the both of the feed-forward and feedback connections. The generalization ability of 3LRNN is compared with one of 3-layer feed-forward neural networks through the computer simulations. It is shown that 3LRNN are not only almost equivalent to 3LFNN but also much superior to one on a certain condition from the viewpoint of the generalization capability. Furthermore, we investigate the generalization ability of 3LRNN with the neurons that have the step functions as the input-output property
Keywords :
digital simulation; feedback; learning (artificial intelligence); recurrent neural nets; 3-layer recurrent neural networks; computer simulations; feed-forward neural networks; generalization ability; Artificial neural networks; Computer simulation; Feedforward neural networks; Feedforward systems; Iterative algorithms; Multi-layer neural network; Neural networks; Neurofeedback; Neurons; Recurrent neural networks;
Conference_Titel :
Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on
Conference_Location :
Honolulu, HI
Print_ISBN :
0-7803-7278-6
DOI :
10.1109/IJCNN.2002.1007641
