2004 IEEE International Conference onComputational Intelligence for Measurement Systems and Applications, 2004. CIMSA.最新文献_第2页

A neural network based intelligent model reference adaptive controller 一种基于神经网络的智能模型参考自适应控制器

2004 IEEE International Conference onComputational Intelligence for Measurement Systems and Applications, 2004. CIMSA. Pub Date : 2004-07-14 DOI: 10.1109/CIMSA.2004.1397257

S. Kamalasadan, A. Ghandakly

{"title":"A neural network based intelligent model reference adaptive controller","authors":"S. Kamalasadan, A. Ghandakly","doi":"10.1109/CIMSA.2004.1397257","DOIUrl":"https://doi.org/10.1109/CIMSA.2004.1397257","url":null,"abstract":"This paper presents a novel neural network based intelligent model reference adaptive controller. In this scheme the intelligent supervisory loop (ISL) is incorporated into the traditional model reference adaptive controller (MRAC) framework by utilizing an online growing dynamic radial basis function neural network (RBFNN) structure in parallel with it. The idea is to control the plant by a direct MRAC with a suitable single reference model, and at the same time respond to plant multimodal dynamics by on line tuning of an RBFNN controller. This parallel RBFNN controller is designed in order to precisely track the system output to the desired command signal trajectory, regardless of system multimodality and/or unmodeled dynamics. The updating details of the RBFNN width, centers and weights are derived to ensure error reduction and for improved tracking accuracy. The importance of the proposed scheme is in its ability to perform effectively even when the plant mode swings without using multiple model concept or a multiple reference model adaptive controller if a suitable reference model structure can be established. Further, the parallel controller will be able to precisely track the reference trajectory even with system showing unmodeled dynamics. The performance ability of the scheme is confirmed by applying to control the angular position of the robotic manipulator under tip load variations.","PeriodicalId":102405,"journal":{"name":"2004 IEEE International Conference onComputational Intelligence for Measurement Systems and Applications, 2004. CIMSA.","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115060626","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 26

Crosstalk-driven placement based on genetic algorithms 基于遗传算法的相声驱动布局

2004 IEEE International Conference onComputational Intelligence for Measurement Systems and Applications, 2004. CIMSA. Pub Date : 2004-07-14 DOI: 10.1109/CIMSA.2004.1397233

Masaya Yoshikawa, H. Terai

引用次数: 1

A basic ontology for multi agent system communication in an environmental monitoring system 环境监测系统中多智能体系统通信的基本本体

2004 IEEE International Conference onComputational Intelligence for Measurement Systems and Applications, 2004. CIMSA. Pub Date : 2004-07-14 DOI: 10.1109/CIMSA.2004.1397228

V. Di Lecce, C. Pasquale, V. Piuri

引用次数: 21

A rail damage detection and measurement system using neural networks 基于神经网络的钢轨损伤检测与测量系统

2004 IEEE International Conference onComputational Intelligence for Measurement Systems and Applications, 2004. CIMSA. Pub Date : 2004-07-14 DOI: 10.1109/CIMSA.2004.1397218

Z. Hou, M. Gupta

引用次数: 5

Influence of forcings and circulation patterns on mean temperatures at different scales: an analysis by neural network modeling 强迫和环流型态对不同尺度平均温度的影响:神经网络模拟分析

2004 IEEE International Conference onComputational Intelligence for Measurement Systems and Applications, 2004. CIMSA. Pub Date : 2004-07-14 DOI: 10.1109/CIMSA.2004.1397229

A. Pasini, M. Loré, F. Ameli

引用次数: 2

Nuclear steam generator level control by a neural network-tuning 2-DOF PID controller 核蒸汽发生器液位控制采用神经网络自整定二自由度PID控制器

2004 IEEE International Conference onComputational Intelligence for Measurement Systems and Applications, 2004. CIMSA. Pub Date : 2004-07-14 DOI: 10.1109/ICNSC.2004.1297115

Dong Hwa Kim

{"title":"Nuclear steam generator level control by a neural network-tuning 2-DOF PID controller","authors":"Dong Hwa Kim","doi":"10.1109/ICNSC.2004.1297115","DOIUrl":"https://doi.org/10.1109/ICNSC.2004.1297115","url":null,"abstract":"This paper focuses on the level control of a steam generator in a nuclear power plant. It is very difficult to effectively control the level of the nuclear steam generator, because of the swelling and shrinking caused by many kinds of disturbances, such as feed water rate, feed water temperature, main steam flow rate, and coolant temperature. Up to the present time, the PI controller has been used for the level control, owing to the easy control algorithms and the advantage which have been proven on the nuclear power plant. However, since there are problems with stability control during low power and start-up, only a highly experienced operator can operate during those procedures. A great deal of time and an expensive simulator is needed for the training of an operator. In addition to studying this problem, this paper has studied the tuning of a 2-DOF PID (two-degrees of freedom PID) controller by a neural network in the level control of the steam generator of a nuclear power plant, through the simulation and the experimentation of the model steam generator. Results obtained by simulation reveal the importance of the controller tuning in nuclear steam generator level control, and results obtained on the experimental steam generator suggest methods which can be used to reduce swelling and shrinking.","PeriodicalId":102405,"journal":{"name":"2004 IEEE International Conference onComputational Intelligence for Measurement Systems and Applications, 2004. CIMSA.","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131037870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 14

Financial prediction using modified probabilistic learning network with embedded local linear models 基于嵌入局部线性模型的改进概率学习网络的金融预测

2004 IEEE International Conference onComputational Intelligence for Measurement Systems and Applications, 2004. CIMSA. Pub Date : 2004-07-14 DOI: 10.1109/CIMSA.2004.1397236

T. Jan, T. Yu, J. Debenham, S. Simoff

引用次数: 6

Implementation method for voting of neural networks 神经网络投票的实现方法

2004 IEEE International Conference onComputational Intelligence for Measurement Systems and Applications, 2004. CIMSA. Pub Date : 2004-07-14 DOI: 10.1109/CIMSA.2004.1397240

Huang Qian, Zheng Qilun, Fan Wenhao

引用次数: 2

A functional model based on single unit recordings from Parkinsonian brain 一个基于帕金森大脑单个单元记录的功能模型

2004 IEEE International Conference onComputational Intelligence for Measurement Systems and Applications, 2004. CIMSA. Pub Date : 2004-07-14 DOI: 10.1109/CIMSA.2004.1397224

S. Leondopulos, E. Micheli-Tzanakou

引用次数: 2

A rough-GA hybrid algorithm for rule extraction from large data 基于粗糙遗传算法的大数据规则提取

2004 IEEE International Conference onComputational Intelligence for Measurement Systems and Applications, 2004. CIMSA. Pub Date : 2004-07-14 DOI: 10.1109/CIMSA.2004.1397237

G. Chakraborty, B. Chakraborty

引用次数: 5