文档介绍：第3章 Fuzzy Identification and Estimation
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教学内容:
这一章主要研究了怎样用模糊进行评估和辨识。模糊辨识设计的最主要的问题是怎样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,然后介绍了传统的辨识方法: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。
本章将现代控制理论的自适应控制理论应用于模糊控制技术中,以期提高模糊控制系统的动态、静态性能,增强模糊控制系统对于模型的参数、结构变化的鲁棒性以及控制系统不同相应阶段对控制其性能的不同要求,提高控制系统的综合品质。
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主要内容:
直接自适应控制和间接自适应控制的基本概念;
模糊模型参考学习控制( FMRLC )的原理、结构;
FMRLC 的设计和实现。
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教学重点:
详细阐述了用一个函数拟和输入输出数据所应注意的问题,同时还阐述了怎样将语言变量信息转化为函数,以便用该函数来拟和输入输出数据。其中输入输出数据的选择关系着辨识结果的好坏,通常要求其能对系统充分激励。
   模糊模型参考学习控制(FMRLC)的原理和结构 。
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教学难点:
对FMRLC中学习机制的准确把握和理解,关键是模糊逆模型的设计。
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教学要求:
本章的学习需要预先掌握一定的自适应控制、自校正调节器的基础知识、概念。要求掌握模糊模型参考学习控制(FMRLC)的原理和结构。
当离散数据是通过试验获得的输入输出数据的时候,我们就可以通过数据来辨识一个模糊系统的模型;当数据是通过其它的途径获得时,可以对数据进行插补处理,然后用处理过的数据来辨识模糊系统。
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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-makin