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Fundamental Research on Learning and Intelligence in System Control

系统控制中的学习与智能基础研究

基本信息

批准号：
02452180
负责人：
YAMAMOTO Yutaka
金额：
$ 3.26万
依托单位：
KYOTO UNIVERSITY
依托单位国家：
日本
项目类别：
Grant-in-Aid for General Scientific Research (B)
财政年份：
1990
资助国家：
日本
起止时间：
1990 至 1992
项目状态：
已结题

项目摘要

The focus of this research project is two fold: one is the general treatment of learning control scheme and the other is the study of learning mechanism of neural networks viewed from the system theoretic viewpoint. Needless to say, these two issues are mutually related.In the study of the general learning control scheme, results on stability conditions in the frequency domain, robust stability condition under plant perturbations are obtained. these results guaranteed that robustness analysis can be made for such learning schemes as modified repetitive control schemes based upon the methodology already employed for finite-dimensional systems.In relation to these, it is also clarified that the learning mechanism of neural networks and its application to control systems can be unified into the principle that the association of unorganized memories and the optimization in their respective parameter space. The notion of composite networks to be described below is the key to this development.In the study of neural networks, the fundamental standpoint of viewing networks as dynamical systems led to the following subjects: 1) derivation of a learning algorithm for general networks with feedback connections, 2) its application to pattern recognition, 3) its further generalization to composite networks, 4) application of more effective algorithms such as the conjugate gradient method, each leading to a satisfactory result. In particular, the composite networks enjoy such features as 1) making learning easier by decomposing the problem into smaller parts, 2) applicable to networks where teaching signals are implicit, and are expected to become more fundamental to various construction of neural networks.On the other hand, it has also become clear that more theoretical analysis is fairly difficult, partly due to the fundamental nonlinearity. More detailed research in this direction can be an open problem for the future.

本课题的研究重点有两个方面：一是学习控制方案的一般处理，二是从系统论的观点研究神经网络的学习机制。在一般学习控制方案的研究中，得到了系统在频域稳定性条件、系统摄动下的鲁棒稳定性条件等方面的结果。这些结果保证了在有限维系统的研究方法的基础上，对诸如改进的重复控制等学习方案进行鲁棒性分析，并阐明了神经网络的学习机制及其在控制系统中的应用可以统一为无组织记忆的结合和在各自的参数空间中的优化原理。下面要介绍的复合网络的概念是这一发展的关键。在神经网络的研究中，将网络视为动力系统的基本观点导致了以下主题：1）导出一般反馈网络的学习算法，2）将其应用于模式识别，3）将其进一步推广到组合网络，（4）应用更有效的算法，如共轭梯度法，每种算法都能得到令人满意的结果。特别是，复合网络具有以下特点：1）通过将问题分解为更小的部分，使学习变得更容易; 2）适用于教学信号是隐含的网络，有望成为各种神经网络构造的基础。另一方面，由于基本非线性，理论分析相当困难。在这个方向上进行更详细的研究可能是未来的一个开放问题。