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

Neuro-fuzzy tuning of PID controller for control of actual gas turbine power 燃气轮机实际功率PID控制器的神经模糊整定

2004 IEEE International Conference onComputational Intelligence for Measurement Systems and Applications, 2004. CIMSA. Pub Date : 2004-07-16 DOI: 10.1109/CIMSA.2004.1397260

Kim Dong Hwa

{"title":"Neuro-fuzzy tuning of PID controller for control of actual gas turbine power","authors":"Kim Dong Hwa","doi":"10.1109/CIMSA.2004.1397260","DOIUrl":"https://doi.org/10.1109/CIMSA.2004.1397260","url":null,"abstract":"The purpose of introducing combined cycle with gas turbine in power plants is to reduce losses of energy. Their main role lies in the utilization of waste heat that may be found in exhaust gases from the gas turbine or at some other points of the process to produce additional electricity. The efficiency of the plant increases reaching over 50%, while the traditional steam turbine plants is approximately 35%/spl ap/40% or so. Up to date, the PID controller has been used to operate under such systems, but since the gain of PID controller manually has to be tuned by trial and error procedures. Getting an optimal PID gains is very difficult to tune manually without control design experience. In this paper, we studied an acquiring of transfer function from operating data of Gun-san gas turbine in Korea and a new 2-DOF PID controller tuning by NFS is designed for the optimum control to Guns-san gas turbine's variables variety. Since the shape of a membership function in the NFS vary on the characteristics of plant. ANFIS based control method is effective for plant that their variables vary. Its results are compared to the conventional PID, 2-DOF PID controller and represents satisfactory response. We expect this method will be used for another process because it is studied on the real operating data.","PeriodicalId":102405,"journal":{"name":"2004 IEEE International Conference onComputational Intelligence for Measurement Systems and Applications, 2004. CIMSA.","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121590702","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: 13

Synthesis of formal and fuzzy logic to detect patterns in clutter 综合形式逻辑和模糊逻辑来检测杂乱中的模式

2004 IEEE International Conference onComputational Intelligence for Measurement Systems and Applications, 2004. CIMSA. Pub Date : 2004-07-16 DOI: 10.1109/CIMSA.2004.1397259

L. Perlovsky, R. Linnehan, C. Mutz, J. Schindler, B. Weijers, R. Brockett

{"title":"Synthesis of formal and fuzzy logic to detect patterns in clutter","authors":"L. Perlovsky, R. Linnehan, C. Mutz, J. Schindler, B. Weijers, R. Brockett","doi":"10.1109/CIMSA.2004.1397259","DOIUrl":"https://doi.org/10.1109/CIMSA.2004.1397259","url":null,"abstract":"Recognizing patterns in data often relies on rules, or exploits simple features in the data. However, when noise or clutter obscures these features in the data, one must consider a number of different features to determine the best match. This often leads to combinatorial complexity manifested in either of two ways, complexity of learning or complexity of computations. Adaptive model-based approaches potentially offer better computational performance than feature-based methods and may lead to extracting the maximum information from data. These techniques still often relied on using formal logic to compare library models to incoming data. Neural networks are usually not easy for implementing model-based approaches. Fuzzy logic bypasses using formal logic, but it provides solutions that often are heavily influenced by the initial degree of fuzziness. We are developing a technique for detecting patterns below clutter based on the neural network modeling field theory. Modeling field theory (MFT) using fuzzy dynamic logic to overcome combinatorial complexity is introduced along with an algorithm suitable for the detection of patterns below clutter. This new mathematical technique is inspired by the analysis of biological systems, like the human brain, which combines conceptual understanding with emotional evaluation and overcomes the combinatorial complexity of model-based techniques.","PeriodicalId":102405,"journal":{"name":"2004 IEEE International Conference onComputational Intelligence for Measurement Systems and Applications, 2004. CIMSA.","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114733488","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: 4

A fuzzy multiple reference model adaptive control scheme for flexible link robotic manipulator 柔性连杆机器人模糊多参考模型自适应控制方案

2004 IEEE International Conference onComputational Intelligence for Measurement Systems and Applications, 2004. CIMSA. Pub Date : 2004-07-14 DOI: 10.1109/CIMSA.2004.1397255

S. Kamalasadan, A. Ghandakly, K. Al-Olimat

{"title":"A fuzzy multiple reference model adaptive control scheme for flexible link robotic manipulator","authors":"S. Kamalasadan, A. Ghandakly, K. Al-Olimat","doi":"10.1109/CIMSA.2004.1397255","DOIUrl":"https://doi.org/10.1109/CIMSA.2004.1397255","url":null,"abstract":"In this paper a novel fuzzy logic based multiple reference model adaptive controller approach for the position control of a single link robotic manipulator is presented. The proposed fuzzy logic scheme is used for generating multiple reference models, within the model reference adaptive control (MRAC) framework, in response to changes in modes of operation or modal swings due to manipulator tip load variation. Thus the scheme is utilized to generate dynamic reference model and the overall structure is coined as fuzzy multiple reference model adaptive controller (FMRMAC). Following a rule base the fuzzy switching scheme effectively monitors changes in operating conditions due to tip load variation. A fuzzy inference engine then fires appropriate rules, which gives a fuzzified output value. Further defuzzification is performed to switch the reference model in a predefined domain. The main contribution of the paper is that the proposed approach can be performed online and is very well suitable for plants showing sudden 'jump' in operating conditions. Unlike, static multiple model algorithms for switching (noninteracting individual model-based filters) or switching dynamic algorithms (susceptible to numerical overflow), this scheme provides an interactive multiple model environment with soft switching. This approach is found to be every effective and fault tolerant.","PeriodicalId":102405,"journal":{"name":"2004 IEEE International Conference onComputational Intelligence for Measurement Systems and Applications, 2004. CIMSA.","volume":"57 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":"127344996","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: 20

Evolution of optimised structuring element sequences for high-speed embedded vision applications 高速嵌入式视觉应用中优化结构元素序列的演化

2004 IEEE International Conference onComputational Intelligence for Measurement Systems and Applications, 2004. CIMSA. Pub Date : 2004-07-14 DOI: 10.1109/CIMSA.2004.1397251

A. Magnusson, Ian P. W. Sillitoe

引用次数: 1

Robustness test of CMOS circuit based on its worst case power consumption signature using ATE and GA-MIE technique 基于最坏情况功耗特征的CMOS电路稳健性测试采用ATE和GA-MIE技术

2004 IEEE International Conference onComputational Intelligence for Measurement Systems and Applications, 2004. CIMSA. Pub Date : 2004-07-14 DOI: 10.1109/CIMSA.2004.1397239

E. Liau, D. Schmitt-Landsiedel

{"title":"Robustness test of CMOS circuit based on its worst case power consumption signature using ATE and GA-MIE technique","authors":"E. Liau, D. Schmitt-Landsiedel","doi":"10.1109/CIMSA.2004.1397239","DOIUrl":"https://doi.org/10.1109/CIMSA.2004.1397239","url":null,"abstract":"This paper presents a diagnosis method which works with industrial semiconductor ATE for analyzing the robustness of the circuit and uses genetic algorithm (GA) with a novel multiple individuals (chromosomes) evolution (GA-MIE) technique. The term robustness in this paper refers to stability and performance of circuits with multiple sources of uncertainties. The objective is studying the worst case activity on chip based on its worst case power consumption signature with respect to a set of worst case input tests. Tests are referred to input patterns and test conditions, since the activity of CMOS circuit is a complex function of the input tests and operating parameters. For instance, the timing and voltage levels on chip can vary due to a small variation of input timing and voltage level. Traditional test and analysis approaches do not consider test condition variation. Experimental results on a test chip show the worst case active tests generated with our approach provoke the device to run slower than normal tests using typical approaches.","PeriodicalId":102405,"journal":{"name":"2004 IEEE International Conference onComputational Intelligence for Measurement Systems and Applications, 2004. CIMSA.","volume":"12 11 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":"132020432","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: 0

Fuzzy calibration for pulsed time-of-flight laser rangefinder 脉冲飞行时间激光测距仪的模糊定标

2004 IEEE International Conference onComputational Intelligence for Measurement Systems and Applications, 2004. CIMSA. Pub Date : 2004-07-14 DOI: 10.1109/CIMSA.2004.4557941

T. Sondej, R. Pelka

引用次数: 1

On-line fuzzy neural modeling with structure and parameters updating 基于结构和参数更新的在线模糊神经网络建模

2004 IEEE International Conference onComputational Intelligence for Measurement Systems and Applications, 2004. CIMSA. Pub Date : 2004-07-14 DOI: 10.1109/CIMSA.2004.1397247

A. Ferreyra, W. Yu

引用次数: 1

Classification methods, reduced datasets and quality analysis applications 分类方法，简化数据集和质量分析应用

2004 IEEE International Conference onComputational Intelligence for Measurement Systems and Applications, 2004. CIMSA. Pub Date : 2004-07-14 DOI: 10.1109/CIMSA.2004.1397246

C. Alippi, P. Braione

引用次数: 0

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

Edge feature extraction in digital images with the ant colony system 基于蚁群系统的数字图像边缘特征提取

2004 IEEE International Conference onComputational Intelligence for Measurement Systems and Applications, 2004. CIMSA. Pub Date : 2004-07-14 DOI: 10.1109/CIMSA.2004.1397248

X. Zhuang

引用次数: 51