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  1. Science in China Series : Information Sciences
  2. Science in China Series : Information Sciences : Volume 44
  3. Science in China Series : Information Sciences : Volume 44, Issue 5, October 2001
  4. A recurrent stochastic binary network
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Science in China Series : Information Sciences : Volume 61
Science in China Series : Information Sciences : Volume 60
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Science in China Series : Information Sciences : Volume 58
Science in China Series : Information Sciences : Volume 57
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Science in China Series : Information Sciences : Volume 47
Science in China Series : Information Sciences : Volume 46
Science in China Series : Information Sciences : Volume 45
Science in China Series : Information Sciences : Volume 44
Science in China Series : Information Sciences : Volume 44, Issue 6, December 2001
Science in China Series : Information Sciences : Volume 44, Issue 5, October 2001
F[x]-lattice basis reduction algorithm and multisequence synthesis
Two modified discrete chirp Fourier transform schemes
The schema deceptiveness and deceptive problems of genetic algorithms
Web search engine: Characteristics of user behaviors and their implication
A neuron model with trainable activation function (TAF) and its MFNN supervised learning
A recurrent stochastic binary network
Stability of general neural networks with reaction-diffusion
Noise estimation for deep sub-micron integrated circuits
Science in China Series : Information Sciences : Volume 44, Issue 4, August 2001
Science in China Series : Information Sciences : Volume 44, Issue 3, June 2001
Science in China Series : Information Sciences : Volume 44, Issue 2, April 2001
Science in China Series : Information Sciences : Volume 44, Issue 1, February 2001

A recurrent stochastic binary network

Content Provider Springer Nature Link
Author Zhao, Jieyu
Copyright Year 2001
Abstract Stochastic neural networks are usually built by introducing random fluctuations into the network. A natural method is to use stochastic connections rather than stochastic activation functions. We propose a new model in which each neuron has very simple functionality but all the connections are stochastic. It is shown that the stationary distribution of the network uniquely exists and it is approximately a Boltzmann-Gibbs distribution. The relationship between the model and the Markov random field is discussed. New techniques to implement simulated annealing and Boltzmann learning are proposed. Simulation results on the graph bisection problem and image recognition show that the network is powerful enough to solve real world problems.
Starting Page 376
Ending Page 388
Page Count 13
File Format PDF
ISSN 10092757
Journal Science in China Series : Information Sciences
Volume Number 44
Issue Number 5
e-ISSN 18622836
Language English
Publisher Science in China Press
Publisher Date 2001-01-01
Publisher Place Beijing
Access Restriction Subscribed
Subject Keyword recurrent stochastic binary network incremental Boltzmann learning Markov random field stimulated annealing Information Systems and Communication Service
Content Type Text
Resource Type Article
Subject Computer Science
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