DocumentCode
328907
Title
A stochastic network with rotor neurons
Author
Parra, Lucas ; Deco, Gustavo
Author_Institution
Corp. Res. & Dev., Siemens AG, Munich, Germany
Volume
2
fYear
1993
fDate
25-29 Oct. 1993
Firstpage
1397
Abstract
We define a new network structure to realize a continuous version of the Boltzmann machine (BM). Based on the mean field (MF) theory for continuous and multidimensional elements, named rotors (introduced by Gislen and Peterson), we derive the corresponding MF learning algorithm. Simulations demonstrate the learning capability of this network for continuous mappings.
Keywords
Boltzmann machines; learning (artificial intelligence); neural nets; Boltzmann machine; continuous elements; continuous mappings; learning algorithm; mean field theory; multidimensional elements; network structure; rotor neurons; stochastic network; Annealing; Convergence; Equations; Multidimensional systems; Neural networks; Neurons; Research and development; Stochastic processes; Temperature; Tin;
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.1993.716805
Filename
716805
Link To Document