Peer-reviewed articles 17,970 +



Title: APPROACH TO THE AUTOMATIC GENERATION OF RULES FOR A FUZZY CONTROLLER IN THE PROCESS OF LEARNING WITH A TEACHER

APPROACH TO THE AUTOMATIC GENERATION OF RULES FOR A FUZZY CONTROLLER IN THE PROCESS OF LEARNING WITH A TEACHER
J. Daeibal;N. Sergeev
1314-2704
English
20
2.1
The authors propose an algorithm for automatically constructing rules for control
subsystems, which can be attributed to fuzzy regulators. Numeric variables used to
obtain information about the control object and control actions by which a human
operator controls the object in the work are presented in the form of linguistic variables.
The membership functions of these variables are constructed as a result of an expert
survey. The example describes the operation of the simplest odd regulator with readymade
rules. Next, the author's algorithm for automatically constructing rules in the
learning process is sequentially presented. The controller is trained in the process of
monitoring the real actions of the human operator.
The challenge is to build a learning mechanism based on a fuzzy controller using
production rules. An important feature of the presented learning mechanism is that at
each step of the training the meaning of the changes that have occurred was understood.
It is important to see that the changes that occur are expressed not only in the change of
coefficients or any functions but are expressed in the form of quite understandable
sentences of a natural language, for example. Such ?understandability? is necessary
when the process of forming a decision or control action is multi-step or multi-stage,
when the goal of the process is to form some chain of reasoning, or an autonomous
object needs to ?understand? what is actually happening.
For simplicity, suppose that we have some informative input quantity and some output
quantity, which can be considered as a control action. The difference from the classical
regulators is that we do not control (or maintain) the value of the output quantity by
influencing the input value through feedback but respond to the value of the input
quantity by changing some output quantity, which may not be directly related to the
input value. For example, in response to an increase in vehicle speed, we increase the
distance to the curb. In this case, to increase the level of travel safety. Those. according
to the values of one quantity, we change the values of another quantity for the sake of
changing some third.
conference
20th International Multidisciplinary Scientific GeoConference SGEM 2020
20th International Multidisciplinary Scientific GeoConference SGEM 2020, 18 - 24 August, 2020
Proceedings Paper
STEF92 Technology
International Multidisciplinary Scientific GeoConference-SGEM
SWS Scholarly Society; Acad Sci Czech Republ; Latvian Acad Sci; Polish Acad Sci; Russian Acad Sci; Serbian Acad Sci & Arts; Natl Acad Sci Ukraine; Natl Acad Sci Armenia; Sci Council Japan; European Acad Sci, Arts & Letters; Acad Fine Arts Zagreb Croatia; C
59-66
18 - 24 August, 2020
website
cdrom
6969
Production rules;UAV;Automatic control systems;Systems learning

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