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https://doi.org/10.17559/TV-20160108193022

Skinner operant conditioning model and robot bionic self-learning control

Jianxian Cai ; Institute of Disaster Prevention, Department of Disaster Prevention Instrument, Sanhe Hebei 065201, China
Li Hong ; Institute of Disaster Prevention, Department of Disaster Prevention Instrument, Sanhe Hebei 065201, China
Lina Cheng ; Institute of Disaster Prevention, Department of Disaster Prevention Instrument, Sanhe Hebei 065201, China
Ruihong Yu ; Institute of Disaster Prevention, Department of Disaster Prevention Instrument, Sanhe Hebei 065201, China


Puni tekst: hrvatski pdf 579 Kb

str. 65-75

preuzimanja: 661

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Puni tekst: engleski pdf 579 Kb

str. 65-75

preuzimanja: 1.660

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Sažetak

A Fuzzy Skinner Operant Conditioning Automaton (FSOCA) is constructed based on Operant Conditioning Mechanism with Fuzzy Set theory. The main character of FSOCA automaton is: the fuzzed results of state by Gaussian function are used as fuzzy state sets; the fuzzy mapping rules of fuzzy-conditioning-operation replace the stochastic "conditioning-operant" mapping sets. So the FSOCA automaton can be used to describe, simulate and design various self-organization actions of a fuzzy uncertain system. The FSOCA automaton firstly adopts online clustering algorithm to divide the input space and uses the excitation intensity of mapping rule to decide whether a new mapping rule needs to be generated in order to ensure that the number of mapping rules is economical. The designed FSOCA automaton is applied to motion balanced control of two-wheeled robot. With the learning proceeding, the selected probability of the optimal consequent fuzzy operant will gradually increase, the fuzzy operant action entropy will gradually decrease and the fuzzy mapping rules will automatically be generated and deleted. After about seventeen rounds of training, the selected probabilities of fuzzy consequent optimal operant gradually tend to one, the fuzzy operant action entropy gradually tends to minimum and the number of fuzzy mapping rules is optimum. So the robot gradually learns the motion balance skill.

Ključne riječi

balanced control; Fuzzy Set; mapping rules; Skinner Operant Conditioning Mechanism

Hrčak ID:

153157

URI

https://hrcak.srce.hr/153157

Datum izdavanja:

19.2.2016.

Podaci na drugim jezicima: hrvatski

Posjeta: 5.890 *