Study of Modeling and Intelligent Control of Complex Systems Using Learning Networks

利用学习网络的复杂系统建模与智能控制研究

基本信息

批准号：
09450171
负责人：
HIRASAWA Kotaro
金额：
$ 6.08万
依托单位：
Kyushu University
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (B)
财政年份：
1997
资助国家：
日本
起止时间：
1997 至 1999
项目状态：
已结题

项目摘要

Since the first proposal of a neuron model by Mc Culloch and Pitts in the 1940's, especially after the revitalization of artificial neural networks in 1980's, a variety of neural networks have been devised and are now applied in many fields. The vast majority of neural networks in use are those networks whose parameters or weights are tuned by gradiant-based supervised learning. This category includes feedfoward networks or multilayer parceptrons, various types of recurrent neural networks, radial basis function networks, fuzzy neural networks, and some networks with special architectures, such as time delay neural networks. These networks seemingly have different architectures and are trained by distinguishable training algorithms. In essence, however, they can be unified in a single framework in regard to both their architectures and learning algorithms. Universal Learning Networks (ULN's) have been proposed, as the name indicates, to provide a universal framework for the class of neural networks and moreover to model and control complex systems because most of the general complex systems in the real world can be modeled by the networks whose nodes represent the processing elements, and the branch between the nodes can describe the relation among the processes. Unification of a variety of network architectures which can describe the complex systems and unification of their learning algorithms are an objective of ULN's. This provides a consistent viewpoint for the various kinds of networks.

自从1940年代，MC Culloch和Pitts对神经元模型的第一个建议以来，尤其是在1980年代人工神经网络振兴之后，已经设计了各种神经网络，现在已在许多领域中应用。使用的绝大多数神经网络都是那些通过基于毕业的监督学习来调整参数或权重的网络。此类别包括馈电网络或多层parceptron，各种类型的复发性神经网络，径向基函数网络，模糊的神经网络以及一些具有特殊体系结构的网络，例如时间延迟神经网络。这些网络似乎具有不同的体系结构，并通过可区分的培训算法进行培训。但是，从本质上讲，就其架构和学习算法而言，它们可以在一个框架中统一。顾名思义，已经提出了通用学习网络（ULN），以为神经网络类提供一个通用框架，此外，以建模和控制复杂系统，因为现实世界中的大多数一般复杂系统可以由节点代表处理元素的网络，节点之间的分支可以描述过程之间的关系。各种网络体系结构的统一，这些网络体系结构可以描述复杂的系统和学习算法的统一，这是ULN的目标。这为各种网络提供了一致的观点。