Propagation and control of stochastic signals through universal learning networks
Propagation and control of stochastic signals through universal learning networks
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DOI：
10.1016/j.neunet.2005.10.005
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发表时间：
2006-05
期刊：
影响因子：
--
通讯作者：
K. Hirasawa;S. Mabu;Jinglu Hu
中科院分区：
文献类型：
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作者：
K. Hirasawa;S. Mabu;Jinglu Hu
提出了利用通用学习网络（ULNs）对随机信号进行传播和控制的方法及其应用。ULN已经被开发成神经网络的超集，并已被应用为非线性大规模复杂系统建模和控制的通用框架。然而，ULNs不能处理随机变量。确定性信号可以通过ULN传播，但ULN不提供信号传播的任何随机特性，因此提出了一种概率通用学习网络（ProbabilityUniversalLearning Networks，PrULNs）方法，该方法可以处理随机变量并训练网络参数，使信号具有预先指定的随机特性。作为该方法的应用实例，研究了带噪声的非线性动态系统的控制与辨识问题，结果表明，该方法对于处理带噪声的非线性随机系统的控制与辨识问题是有效的。
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.