An Overview and Practical Application of Biological Intelligence Algorithm Used in Intelligence Control

被引:6
作者
Chen, Jie [1 ]
Cheng, Sheng [2 ]
Xu, Meng [3 ]
机构
[1] China Manned Space Engn Off, Beijing 100083, Peoples R China
[2] China Aerosp Sci & Technol Corp, Software R&D Ctr, Beijing 100094, Peoples R China
[3] Northwestern Polytech Univ, Sch Comp Sci, Xian 710072, Shaanxi, Peoples R China
来源
PROCEEDINGS OF 2018 THE 2ND INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND ARTIFICIAL INTELLIGENCE (CSAI 2018) / 2018 THE 10TH INTERNATIONAL CONFERENCE ON INFORMATION AND MULTIMEDIA TECHNOLOGY (ICIMT 2018) | 2018年
关键词
Intelligent control; Biological intelligence; Automatic control; Brain-inspired Intelligence; Reinforcement learning;
D O I
10.1145/3297156.3297175
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Because of the disadvantages of the classical control methods, such as its fixed parameters, so the control effect of the classical control methods is greatly limited, and the Biological Intelligence Algorithm has provided a new way to break the bottleneck of classical control methods because of its adaptive and learning mechanism. With the improvement of the theory of reinforcement learning and deep learning, these theories have greatly improved the performance of the Biological Intelligence Algorithm. This paper summarizes seven kinds of intelligent algorithms which is used in intelligence control commonly, and emphatically analyzes the application examples of the combination of classical automatic control method and intelligent algorithms, especially reinforcement learning. The development status and future development trend of intelligent control based on reinforcement learning, deep learning and Brain-inspired Intelligence Technology in recent years are discussed for the first time in this paper. The purpose of this paper is to emphasize a new idea of combining intelligent algorithm with classical control method, and provide new ideas and practical examples for the research of intelligent control.
引用
收藏
页码:200 / 206
页数:7
相关论文
共 27 条
[11]   Seeker optimization algorithm [J].
Dai, Chaohua ;
Chen, Weirong ;
Zhu, Yunfang .
2006 INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND SECURITY, PTS 1 AND 2, PROCEEDINGS, 2006, :225-229
[12]   Modeling Intelligent Decision-Making Command and Control Agents: An Application to Air Defense [J].
Das, Sumanta Kumar .
IEEE INTELLIGENT SYSTEMS, 2014, 29 (05) :22-29
[13]  
Director& nbsp Research&, ARTIFICIAL INTELLIGE
[14]   Multi-modal perception [J].
Hollier, MP ;
Rimell, AN ;
Hands, DS ;
Voelcker, RM .
BT TECHNOLOGY JOURNAL, 1999, 17 (01) :35-46
[15]  
Jiao L C, 2016, CHINESE J COMPUTERS
[16]   ImageNet Classification with Deep Convolutional Neural Networks [J].
Krizhevsky, Alex ;
Sutskever, Ilya ;
Hinton, Geoffrey E. .
COMMUNICATIONS OF THE ACM, 2017, 60 (06) :84-90
[17]   Deep learning [J].
LeCun, Yann ;
Bengio, Yoshua ;
Hinton, Geoffrey .
NATURE, 2015, 521 (7553) :436-444
[18]  
Ling Wei-ye, 2003, Proceedings of the CSEE, V23, P98
[19]   Human-level control through deep reinforcement learning [J].
Mnih, Volodymyr ;
Kavukcuoglu, Koray ;
Silver, David ;
Rusu, Andrei A. ;
Veness, Joel ;
Bellemare, Marc G. ;
Graves, Alex ;
Riedmiller, Martin ;
Fidjeland, Andreas K. ;
Ostrovski, Georg ;
Petersen, Stig ;
Beattie, Charles ;
Sadik, Amir ;
Antonoglou, Ioannis ;
King, Helen ;
Kumaran, Dharshan ;
Wierstra, Daan ;
Legg, Shane ;
Hassabis, Demis .
NATURE, 2015, 518 (7540) :529-533
[20]  
Oroojlooyjadid A., 2017, A Deep Q-Network for the Beer Game: A Reinforcement Learning algorithm to Solve Inventory Optimization Problems