Propagation and control of stochastic signals through universal learning networks

Kotaro Hirasawa, Shingo Mabu, Jinglu Hu

Research output: Contribution to journalArticle

19 Citations (Scopus)

Abstract

The way of propagating and control of stochastic signals through Universal Learning Networks (ULNs) and its applications are proposed. ULNs have been already developed to form a superset of neural networks and have been applied as a universal framework for modeling and control of non-linear large-scale complex systems. However, the ULNs cannot deal with stochastic variables. Deterministic signals can be propagated through a ULN, but the ULN does not provide any stochastic characteristics of the signals propagating through it. The proposed method named Probabilistic Universal Learning Networks (PrULNs) can process stochastic variables and can train network parameters so that the signals behave with the pre-specified stochastic properties. As examples of applications of the proposed method, control and identification of non-linear dynamic systems with noises are studied, and it is shown that the method are useful for dealing with the control and identification of the non-linear stochastic systems contaminated with noises.

Original languageEnglish
Pages (from-to)487-499
Number of pages13
JournalNeural Networks
Volume19
Issue number4
DOIs
Publication statusPublished - 2006 May 1

Keywords

  • Forward propagation
  • Gradient method
  • Neural networks
  • Non-linear systems
  • Probabilistic networks
  • Stochastic signals

ASJC Scopus subject areas

  • Cognitive Neuroscience
  • Artificial Intelligence

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