Propagation and control of stochastic signals through universal learning networks

Kotaro Hirasawa*, Shingo Mabu, Jinglu Hu


研究成果: Article査読

21 被引用数 (Scopus)


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.

ジャーナルNeural Networks
出版ステータスPublished - 2006 5月

ASJC Scopus subject areas

  • 認知神経科学
  • 人工知能


「Propagation and control of stochastic signals through universal learning networks」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。