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.

该研究项目的重点是两个方面：一个是学习控制方案的一般治疗方法，另一个是从系统理论观点查看的神经网络的学习机制的研究。不用说，这两个问题是相互关联的。在一般学习控制方案的研究中，频域中的稳定性条件的结果获得了植物扰动下的稳健稳定性条件。这些结果确保可以根据已经用于有限维系统的方法的方法来进行此类学习方案，例如修改的重复控制方案。在这些方法中，与这些方法相关的是，神经网络的学习机理及其在控制系统中的应用机理可以统一到无组织的记忆和优化及其优化的主要参数的原则。 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令人满意的结果。特别是，复合网络享有诸如1）通过将问题分解为较小的部分来使学习变得更容易的功能，2）适用于教学信号隐含的网络，并有望变得更加重要，这对于另一方面的各种神经网络的构建。也很明显，由于基本的非线性非线性，因此更加困难的理论分析是相当困难的。朝这个方向进行更详细的研究可能是未来的一个开放问题。