Learning to imitate stochastic time series in a compositional way by chaos

Neural Netw. 2010 Jun;23(5):625-38. doi: 10.1016/j.neunet.2009.12.006. Epub 2009 Dec 23.


This study shows that a mixture of RNN experts model can acquire the ability to generate sequences that are combination of multiple primitive patterns by means of self-organizing chaos. By training the model, each expert learns a primitive sequence pattern, and a gating network learns to imitate stochastic switching of the multiple primitives via chaotic dynamics, utilizing a sensitive dependence on initial conditions. As a demonstration, we present a numerical simulation in which the model learns Markov chain switching among some Lissajous curves by chaotic dynamics. Our analysis shows that by using a sufficient amount of training data, balanced with the network memory capacity, it is possible to satisfy the conditions for embedding the target stochastic sequences into a chaotic dynamical system. It is also shown that reconstruction of a stochastic time series by a chaotic model can be stabilized by adding a negligible amount of noise to the dynamics of the model.

MeSH terms

  • Algorithms
  • Artificial Intelligence*
  • Automation
  • Computer Simulation
  • Feedback
  • Markov Chains
  • Memory
  • Neural Networks, Computer*
  • Probability
  • Stochastic Processes*
  • Time Factors