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.

大规模复杂控制系统的功能与大脑在识别和控制环境的点上的功能相似。本研究的目的是建立控制方法，该方法具有使用最近开发的大脑科学知识的人类判断的能力。大脑的功能分布。 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在研究非线性不连续功能的模式识别问题，非线性动力学系统的系统识别和控制方面的模式识别问题。从模拟中，已经表明，LPN具有根据输入的价值，换句话说，函数的能力，是在网络中选择适当的路由的能力。还已经表明，在形成非线性不连续功能并识别和控制非线性动态系统的情况下，LPN的性能优于神经网络。因此，基于人类大脑的功能分布和学习能力的新控制方法已经建立了新的控制能力。这些方法可以应用于这些新的控制方法，不仅可以使用这些技术控制控制系统，还可以进行大型系统范围和大型系统。