NDLI: Skinner operant conditioning model and robot bionic self-learning control

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Skinner operant conditioning model and robot bionic self-learning control

Content Provider	Semantic Scholar
Author	Cai, Jianxian Cheng, Lina Yu, Ruihong
Copyright Year	2016
Abstract	Original scientific paper 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 fuzzyconditioning-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.
Starting Page	65
Ending Page	75
Page Count	11
File Format	PDF HTM / HTML
DOI	10.17559/TV-20160108193022
Volume Number	23
Alternate Webpage(s)	http://hrcak.srce.hr/file/225557
Alternate Webpage(s)	https://doi.org/10.17559/TV-20160108193022
Language	English
Access Restriction	Open
Content Type	Text
Resource Type	Article

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