Title :
Covariance phasor neural network as a mean field model
Author :
Takahashi, Haruhisa
Author_Institution :
Dept. of Inf. & Commun. Eng., Univ. of Electro-Commun., Chofu, Japan
Abstract :
Covariance model can represent covariance between two units of stochastic machines as cosine of the phase difference. This enables us to calculate the covariance between two units in a deterministic manner as well as average activation. The covariance model could give an elaborate mean field approximation without invoking a higher order mean field model. A covariance Hebbian self organizing rule and Boltzmann learning rule are then investigated on this model.
Keywords :
Boltzmann machines; Hebbian learning; function approximation; self-organising feature maps; Boltzmann learning rule; Boltzmann machine; Hebbian learning; average activation; covariance model; mean field approximation; phase difference; self organizing rule; stochastic machines; Biological neural networks; Brain modeling; Equations; Humans; Logistics; Neural networks; Neurons; Random processes; Stochastic processes; Timing;
Conference_Titel :
Neural Information Processing, 2002. ICONIP '02. Proceedings of the 9th International Conference on
Print_ISBN :
981-04-7524-1
DOI :
10.1109/ICONIP.2002.1202790
