文档介绍：第3章 Fuzzy Identification and Estimation
教学内容
这一章主要讲述用模糊进行评估和辨识。模糊辨识设计的最主要的问题是用已知的
离散数据构建一个模糊系统。首先介绍最基本的函数近似问题,然后介绍传统的辨识方
法:最小二乘法,即怎样用批量最小二乘法和递归最小二乘法来辨识一个系统以匹配输
入输出数据。最后讲述用这两种方法直接训练模糊系统。
教学重点
重点是最小二乘算法。模糊辨识的最小二乘算法包括成批最小二乘算法和递推最小
二乘算法。
教学难点
对最小二乘算法的准确把握和理解,关键是应用模糊最小二乘法实现模糊辨识和估
计。
教学要求
要求掌握模糊辨识和估计的基本概念和知识,主要掌握最小二乘算法的推导和应
用。
Overview
While up to this point we have focused on control, in this chapter we
will examine how to use fuzzy systems for estimation and identification.
The basic problem to be studied here is how to construct a fuzzy system
from numerical data. This is in contrast to our discussion in Chapters 2
and 3, where we used linguistics as the starting point to specify a fuzzy
system. If the numerical data is plant input-output data obtained from an
experiment, we may identify a fuzzy system model of the plant. This may
be useful for simulation purposes and sometimes for use in a controller.
On the other hand, the data e from other sources, and a fuzzy
system may be used to provide for a parameterized nonlinear function
that fits the data by using its basic interpolation capabilities. For instance,
suppose that we have a human expert who controls some process and we
observe how she or he does this by observing what numerical plant input
the expert picks for the given numerical data that she or he observes.
Suppose further that we have many such associations between
"decision-making data." The methods in this chapter will show how to
construct rules for a fuzzy controller from this data (., identify a
controller from the human-generated decision-making data), and in this
sense they provide another method to design controllers.
Yet another problem that can be solved with the methods in this
chapter is that of how to construct a fuzzy system that will serve as a
parameter estimator. To do this, we need data that shows, roughly how
the input-output mapping of the estimator should behave