Publication detail

Learning in Mechatronic Conceptions

BŘEZINA, T.

Original Title

Learning in Mechatronic Conceptions

Type

journal article - other

Language

English

Original Abstract

Mechatronic conceptions are most frequently characterized as synergistic conjunction of the mechanics, electrotechnics and computer science. Computer science as a platform of the realization of control algorithms especially increasingly runs the soft computing algorithms. Soft computing differs from conventional (hard) computing in the basic principle: it exploits the tolerance for imprecision, uncertainty and partial truth to achieve tractability, robustness and low solution cost. The most important components of soft computing are fuzzy logic, neural network theory, probabilistic reasoning, genetic algorithm, chaos theory and parts of machine learning theory. Fundamental issue is that the principal contributions of cited components are complementary, not competitive (leading on hybrid systems creation, etc.). The survey of the most interesting ideas of learning used in soft computing is introduced in this contribution.

Keywords

Q-learning, computer science, control algorithms

Authors

BŘEZINA, T.

RIV year

2001

Released

1. 12. 2001

ISBN

1210-2717

Periodical

Inženýrská mechanika - Engineering Mechanics

Year of study

8

Number

6

State

Czech Republic

Pages from

431

Pages to

442

Pages count

12

BibTex

@article{BUT40192,
  author="Tomáš {Březina}",
  title="Learning in Mechatronic Conceptions",
  journal="Inženýrská mechanika - Engineering Mechanics",
  year="2001",
  volume="8",
  number="6",
  pages="12",
  issn="1210-2717"
}