Applied-Information Technology and Parallel Learning Rules in Intelligent Control System

Wei Qiong Ye; Kai Li Cheng; Lin Xu

doi:10.4028/www.scientific.net/AMR.908.539

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HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 908Applied-Information Technology and Parallel...

Applied-Information Technology and Parallel Learning Rules in Intelligent Control System

Abstract:

This paper applied information technology and takes the exploratory action or state transfer formed experiences of control system as the basis, in the unsupervised condition obtaining planning and controlling rules from experiences as the control knowledge. In learning process, experiences will deduce to rules, then generate high-level concepts or rules, constitute multi-resolution knowledge architecture. In the experiment, mobile robot effective learning and planning system state and action control in quasi-optimal manner based with random experiences.

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Periodical:

Advanced Materials Research (Volume 908)

Pages:

539-542

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.908.539

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Online since:

March 2014

Authors:

Wei Qiong Ye*, Kai Li Cheng, Lin Xu

Keywords:

Control Rule, Parallel Learning, Random Experience, Unsupervised Learning

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* - Corresponding Author

References

[1] Wang ri and Zhu jiayuan, Multiple Attribute Information Decision Making Mode and Application Via Improved Fast Unsupervised Learning Algorithm, Computer engineering and application, 2008, 21: 12~l3, 253.

Google Scholar

[2] Fu Yan and Zhou Junlin, Research of Blind Source Separation Technology which Based on Unsupervised Learning, Journal of UEST of China[J], 2012, Vol. 33, No. 1, 63~66.

Google Scholar

[3] Zhao Wenbo, Huang Deshuang and WangShukun, PR & AI[J], 2012, Vol. 16, No. 4, 442~447.

Google Scholar

[4] He Lin and Hou Hanqing, Automatic Multi-layer Classification Method Based on Integration of Machine Learning and Indexing Experience, Journal Of The China Society For Scientific And Technical Information[J], 2006, Vol. 25, No. 6, 725~729.

Google Scholar

[5] WANG Ming – yan, Learning Control Based on Experienced Data in a Load Simulator, Control Theory and Applications[J], 2009, Vol. 24, No. 8, 1~4.

Google Scholar

[6] Yang Sheng-yue, Experience based acquisition of the initial value for the iterative learning control inputs, Control and Decision[J], 2010, Vol. 19, No. 1, 27~30.

Google Scholar