[1]夏琳琳,苗贵娟,初妍,等.基于自适应神经模糊系统的足球机器人射门点的确定[J].智能系统学报,2013,8(02):143-148.[doi:10.3969/j.issn.1673-4785.201203015]
 XIA Linlin,MIAO Guijuan,CHU Yan,et al.Determination of shooting point for soccer robot based upon adaptive neuro-fuzzy in ference system[J].CAAI Transactions on Intelligent Systems,2013,8(02):143-148.[doi:10.3969/j.issn.1673-4785.201203015]
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基于自适应神经模糊系统的足球机器人射门点的确定(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第8卷
期数:
2013年02期
页码:
143-148
栏目:
出版日期:
2013-04-25

文章信息/Info

Title:
Determination of shooting point for soccer robot based upon adaptive neuro-fuzzy in ference system
文章编号:
1673-4785(2013)02-0143-06
作者:
夏琳琳12 苗贵娟1 初妍2 刘惠敏3 焦圣喜1
1.东北电力大学 自动化工程学院,吉林 吉林 132012;
2.哈尔滨工程大学 计算机科学与技术学院,黑龙江 哈尔滨 150001;
 3.青岛农业大学 机电工程学院,山东 青岛 266109
Author(s):
XIA Linlin12 MIAO Guijuan1 CHU Yan2 LIU Huimin3 JIAO Shengxi1
1. School of Automation Engineering, Northeast Dianli University, Jilin 132012, China;
2. College of Computer Science and Technology, Harbin Engineering University, Harbin 150001, China;
3. College of Electromechanical Engineering, Qingdao Agricultural University, Qingdao 266109, China
关键词:
类高斯函数神经模糊推理系统自适应性射门点足球机器人
Keywords:
Gaussian-type function neuro-fuzzy inference system self-adaptiveness shooting point soccer robot
分类号:
TP301.6
DOI:
10.3969/j.issn.1673-4785.201203015
文献标志码:
A
摘要:
针对足球机器人射门行为中运算的高复杂性和反应延迟的局限,引入一种基于类高斯函数的自适应神经模糊推理系统(ANFIS),用于确定最合适的射门点.系统由前件网络和后件网络构成,结合模糊逻辑理论,建立基于人类语言描述的射门行为模型.采用实际的比赛记录作为训练数据,离线地拟合系统输入与输出之间的映射关系,经训练的系统能够自动地调整前期隶属度函数的形状和后期的自适应权值.仿真结果表明,射门成功率和反应速度都能够达到预期的效果,方法的有效性得到了验证.
Abstract:
In order to solve the limitation of the high computational complexity and delayed reaction in the shooting behavior of soccer robots, an adaptive neuro-fuzzy inference system (ANFIS) was proposed. The proposal invokes the Gaussian-type function technology to determine the optimal shoot point. The entire system was composed of the antecedent network and consequent one. The system integrated the fuzzy logic theory, which, lead to the establishment of the behavior model described by human language. Moreover, the training samples were derived from the shoot data of actual medium competitions, along with the implementation of off-line training methods to describe the mapping relationships between inputs and outputs. Once the training process was completed, the system is able to automatically adjust the shape of antecedent membership functions, as well as the consequent weights adaptively. The simulation results demonstrate that the high shooting success rate and reaction speed can be achieved as expected, proving the effectiveness of the proposed approach.

参考文献/References:

[1]HANG Yinping, TANG Zhiyong, PEI Zhongcai. Strategies for shooting based on fuzzy logic and artificial potential\field in robot soccer systems[C]//Proceedings of 2010 International Conference on Computer Application and System Modeling. Taiyuan, China, 2010: 399-403.
[2]BRUCE J, ZICKLER S, LICITRA M, et al. AMDragons: dynamic passing and strategy on a champion robot soccer team[C]//Proceedings of 2008 IEEE International Conference on Robotics and Automation. Pasadena, USA, 2008: 4074-7079.
[3]ZHOU Ping, YU Aihua, WU Mingguang. Motion control of mobile robot for moving object capture and shooting[C]// Proceedings of IEEE International Conference on Industrial Informatics. Singapore, 2006: 1369-1374. 
[4]SHIEH Mingyuan, CHIOU J S, YOU T L, et al. System design and strategy integration for five-on-five robot soccer competition[C]//Proceedings of the 2005 IEEE International Conference on Mechatronics. Taipei, China, 2005:461-466.
[5]MOZAFARI M, FARD A M. An improved fuzzy mechanism for 3D soccer simulation Agent’s shoot skill[C]//Proceedings of 2006 Annual IEEE India Conference. New Delhi, India, 2006: 1-6.
[6]PIAO S, SUN Lining. Robot action acquisition by self-learning fuzzy controller[C]//Proceedings of Fifth International Conference on Fuzzy Systems and Knowledge Discovery. Ji’nan, China, 2008: 241-244.
[7]REDDYB S, KUMAR J S, REDDY K V K. Predication of surface roughness in turning using adaptive neuro-fuzzy inference system[J]. Jordan Journal of Mechanical and Industrial Engineering, 2009, 3(4): 252-259.
[8]AI-HMOUZ A, SHEN Jun, AI-HMOUZ R, et al. Modelling and simulation of an adaptive neuro-fuzzy inference system (ANFIS) for mobile learning[J]. IEEE Transactions on Learning Technologys, 2007, 6(1): 1-14.
[9]BUDIHARTO W, JAZIDIE A, PURWANTO D. Indoor navigation using adaptive neuro fuzzy controller for servant robot[C]//Proceedings of 2010 Second International Conference on Computer Engineering and Applications. Bali Island, Indonesia, 2010: 582-586.
[10]KUSAGUR A, KODAD S F, RAM B V S. Modeling, design and simulation of an adaptive neuro-fuzzy inference system (ANFIS) for speed control of induction motor[J]. International Journal of Computer Applications, 2010, 6(12): 29-44.

备注/Memo

备注/Memo:
收稿日期:2012-03-19.
网络出版日期:2013-04-09. 
基金项目:吉林省教育厅“十一五”科学技术研究计划资助项目(2010075); 黑龙江省自然科学基金资助项目(F200917); 黑龙江省教育厅科学技术研究计划资助项目(11553046). 
通信作者:夏琳琳.
E-mail:xiall521@mail.nedu.edu.cn.
作者简介:
夏琳琳,女,1980年生,副教授.主要研究方向为机器人技术、人工智能.参与吉林省教育厅“十一五”科学技术研究项目1项、吉林省发改委研究项目1项,横向课题3项.发表学术论文20余篇,其中被EI检索10篇.参与编著教材1部.
苗贵娟,女,1985年生,硕士研究生,主要研究方向为机器人技术.
初妍,女,1979年生,讲师,博士,主要研究方向为数据挖掘与人工智能.主持黑龙江省自然科学基金、黑龙江省教育厅科学技术研究项目各1项.发表学术论文20余篇.
更新日期/Last Update: 2013-05-26