DocumentCode
328294
Title
A backpropagation algorithm for neural networks based an 3D vector product
Author
Nitta, Tohru
Author_Institution
Electrotech. Lab., Ibaraki, Japan
Volume
1
fYear
1993
fDate
25-29 Oct. 1993
Firstpage
589
Abstract
A 3D vector version of the backpropagation algorithm is proposed for multilayered neural networks in which vector product operation is performed, and whose weights, threshold values, input and output signals are all 3D real numbered vectors. This new algorithm can be used to learn patterns consisted of 3D vectors in a natural way. The XOR problem was used to successfully test the new formulation.
Keywords
backpropagation; feedforward neural nets; pattern recognition; vectors; 3D vector product; XOR problem; backpropagation algorithm; multilayered neural networks; patterns learning; threshold values; weights; Cities and towns; Computer networks; Joining processes; Laboratories; Multi-layer neural network; Neural networks; Neurons; Petroleum; Quaternions; Testing;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 1993. IJCNN '93-Nagoya. Proceedings of 1993 International Joint Conference on
Print_ISBN
0-7803-1421-2
Type
conf
DOI
10.1109/IJCNN.1993.713984
Filename
713984
Link To Document