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Dynamical control by recurrent neural networks through genetic algorithms
Author(s) -
Kumagai Toru,
Wada Mitsuo,
Hashimoto Ryoichi,
Utsugi Akio
Publication year - 1999
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/(sici)1099-1115(199906)13:4<261::aid-acs546>3.0.co;2-n
Subject(s) - inverted pendulum , recurrent neural network , artificial neural network , computer science , copying , controller (irrigation) , control theory (sociology) , genetic algorithm , operator (biology) , artificial intelligence , algorithm , control (management) , machine learning , nonlinear system , biology , biochemistry , physics , repressor , quantum mechanics , transcription factor , gene , agronomy , genetics
In this study we composed a recurrent neural network learning controller and applied it to the swinging up and stabilization problem of the inverted pendulum. A recurrent neural network was trained by a genetic algorithm which had an internal copy operator or inter‐individual copy operator. An appropriate controller was acquired in a recurrent neural network by training with a simple evaluation function. The recurrent neural network acquired two completely different rules for swinging up and stabilization of a pendulum. It outputted these two rules continuously so that swinging up and stabilization of a pendulum was realized. Internal copy and inter‐individual copy accelerated learning effectively by copying a part of a chromosome. Copyright © 1999 John Wiley & Sons, Ltd.