Self-Constructing Systems and their Applications to System Identification and Control

自构建系统及其在系统辨识与控制中的应用

• 批准号: 07650499
• 负责人: MURATA Junichi
• 金额: $ 1.15万
• 依托单位: KYUSHU UNIVERSITY
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Scientific Research (C)
• 财政年份: 1995
• 资助国家: 日本
• 起止时间: 1995 至 1996
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-07650499/
• 关键词: Self-Constructing Systems; Rule-sets; Neural Networks; Learning; Optimization; Automaton Learning Networks; Universal Learning Networkd; Robust Control; 一般化; 離散事象

Self-Constructing systems construct and after themselves based on observed date. For systems describing nonnumerical phenomena, a procedure was developed that automatically constructs general rule-set models and detects changes in the data, where bilateral flows of information, generalization from the data to concepts and specialization of concepts that contradict to the data, is appropriately controlled. Also, self-constructing methods were developed for two types of neural networks : neural networks that can express both instances and concepts in a uniform manner, and those which can express concepts explicitly with their input gates. Moreover, a structure design method was proposed for neural networks which can easily cope with changes in the data, and a new optimization method was devised that can be employed in neural networks training and leads to acquiring creatively new functions.As a general framework for self-constructing systems, two network-type frameworks were proposed : Universal Learning Networks for representing continuous systems whose behaviors are described by real-valued variables, and Automaton Learning Network for discrete event systems where the variables take discrete values. Both of them have self-constructing and self-altering capability by learning. And efficient learning algorithms were devised. Also, an automatic structure determination method for the networks was developed.One of the major aims of self-constructing systems is to develop control systems that exhibit, by self-alteration, good performance despite of changes in their surrounding situations. Therefore, in the framework of Universal Learning Network, with control of a crane as an example, robust control systems were developed which perform satisfactorily well coping with changes in payload, initial and target positions of the load and external disturbances.

自构建系统根据观察到的数据进行自我构建。对于描述非数值现象的系统，开发了一种程序，可以自动构建通用规则集模型并检测数据的变化，其中双边信息流、从数据到概念的概括以及与数据相矛盾的概念的专业化都得到适当控制。此外，还为两种类型的神经网络开发了自构造方法：可以以统一方式表达实例和概念的神经网络，以及可以用输入门明确表达概念的神经网络。此外，提出了一种可以轻松应对数据变化的神经网络结构设计方法，并设计了一种新的优化方法，可用于神经网络训练并获得创造性的新功能。作为自构建系统的通用框架，提出了两种网络类型框架：用于表示其行为由实值变量描述的连续系统的通用学习网络，以及用于表示行为由实值变量描述的连续系统的自动机学习网络。 变量取离散值的离散事件系统。两者都具有通过学习自我构建和自我改变的能力。并设计了有效的学习算法。此外，还开发了一种网络自动结构确定方法。 自构建系统的主要目标之一是开发控制系统，通过自我改变，尽管周围情况发生变化，仍能表现出良好的性能。因此，在通用学习网络的框架下，以起重机的控制为例，开发了鲁棒控制系统，可以很好地应对有效载荷、负载初始位置和目标位置以及外部干扰的变化。

项目成果

大林正直: "Robust Control Using Second Order Derivatives of Universal Learning Network" Memoirs of Faculty of Engineering,Kyushu University. 56. 1-14 (1996)

Masanao Obayashi：“使用通用学习网络的二阶导数的鲁棒控制”九州大学工学部回忆录 56. 1-14 (1996)。

村田純一: "Determination and Adaptive Alteration of Artificial Neural Network Structures by a Genetic Algorithm with a Controlled Genotype-Phenotype Mapping" Proceedings of 1996 IEEE International Conference on Systems,Man,and Cybernetics. 3. 1690-1695 (1996)

Junichi Murata：“通过具有受控基因型-表型映射的遗传算法来确定和自适应改变人工神经网络结构”1996 年 IEEE 国际系统、人类和控制论会议论文集 3. 1690-1695 (1996)。

Kotaro Hirasawa: "Universal Learning Network Theory" Transactions of Institute of Electrical Engineers of Japan. Vol.116-C. 794-801 (1996)

Kotaro Hirasawa：《通用学习网络理论》日本电气工程师学会汇刊。

Masanao Ohbayashi: "Robust Control for Disturbances of a Nonlinear System using Universal Learning Network" Research Reports on Information Science and Electrical Engineering, Kyushu University. Vol.2. (1997)

Masanao Ohbayashi：“使用通用学习网络对非线性系统的扰动进行鲁棒控制”九州大学信息科学和电气工程研究报告。

Junichi Murata: "An Optimization Method Using Simulated Annealing for Universal Learning Network" Proceedings of the 1995 Korea Automatic Control Conference, International Program. 183-186 (1995)

Junichi Murata：“使用模拟退火进行通用学习网络的优化方法”1995 年韩国自动控制会议国际会议论文集。