权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Research on Control System based on Petri Network Brain Model

基于Petri网络脑模型的控制系统研究

基本信息

批准号：
06452256
负责人：
HIRASAWA Kotaro
金额：
$ 4.29万
依托单位：
KYUSHU 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：
1994
资助国家：
日本
起止时间：
1994 至 1995
项目状态：
已结题

项目摘要

Functions of the large-scale complicated control systems are similer to the functions of the brain in the points of recognizing and controlling environments.The purpose of this research is to establish the control methodology which has the ability of human's judgment using the knowledge of recently developed brain science.Functions of the brain are distributed. First, the fundamental model for controlling large-scale complicated systems is studied considering the functions distribution of the brain and introducing the ability of learning.The model is named LPN (Learning Petri Network) because it is based on Petri Network and the learning ability is attached to it.It is the most important feature that the specific learning algorithms with teacher and self-orgnizing scheme are introduced in LPN in order to realize the functions distribution.Next, simulations are carried out to investigate the peformance of LPN for the problem of pattern recognition of nonlinear discontinuous functions, system identification and control of nonlinear dynamics systems.From simulations, it has been shown that LPN has the ability of selecting appropriate routes in the network depending on the value of inputs, in other words, the ability of the functionsdistribution. It has been also shown that LPN is superior in performance to Neural Network in the point of forming nonlinear discontinuous functions and identifying and controlling nonlinear dynamic systems.Therefore, a new control methodology based on the functions distribution and the learning ability of human brain has been established.This new control methodology can be applied to not only conventioned process control systems but also large-scale complicated control systems which require sophisticated and advanced control functions.

大型复杂控制系统的功能在识别和控制环境方面与人脑的功能有着相似之处，本研究的目的是利用近年来发展起来的脑科学知识，建立具有人类判断能力的控制方法。第一、本文研究了大规模复杂系统控制的基本模型，考虑了脑的功能分布，并引入了学习能力，称之为LPN模型学习Petri网（Learning Petri Network）是基于Petri网的一种网络结构，它具有学习能力，其最大的特点是给出了具体的带教师和自学习的学习算法。本文首先介绍了一种新的线性规划网络（LPN）的基本思想，并在此基础上提出了一种实现函数分配的组织方案，然后对它在非线性间断函数的模式识别、系统辨识和非线性动力学系统的控制等问题上的性能进行了仿真研究，仿真结果表明，事实证明，LPN有能力根据输入值（换句话说，函数分布的能力）在网络中选择适当的路由。在构造非线性不连续函数和辨识控制非线性动态系统方面，线性Petri网比神经网络有上级性能，因此，提出了一种基于人脑功能分布和学习能力的控制方法，这种方法不仅适用于传统的过程控制系统，而且适用于大型工业过程控制系统。规模复杂的控制系统，需要复杂和先进的控制功能。