2013 IEEE Symposium on Computational Intelligence in Control and Automation (CICA)最新文献

A robust fault detection scheme with an application to mobile robots by using adaptive thresholds generated with locally linear models 基于局部线性模型生成自适应阈值的鲁棒移动机器人故障检测方案

2013 IEEE Symposium on Computational Intelligence in Control and Automation (CICA) Pub Date : 2013-04-16 DOI: 10.1109/CICA.2013.6611657

F. Baghernezhad, K. Khorasani

引用次数: 10

Optimization of fuzzy logic controllers with rule base size reduction using genetic algorithms 基于遗传算法的规则库缩减模糊控制器优化

2013 IEEE Symposium on Computational Intelligence in Control and Automation (CICA) Pub Date : 2013-04-16 DOI: 10.1142/S0219622015500273

P. C. Shill, Yoichiro Maeda, K. Murase

{"title":"Optimization of fuzzy logic controllers with rule base size reduction using genetic algorithms","authors":"P. C. Shill, Yoichiro Maeda, K. Murase","doi":"10.1142/S0219622015500273","DOIUrl":"https://doi.org/10.1142/S0219622015500273","url":null,"abstract":"In this paper, we present the automatic design methods with rule base size reduction for fuzzy logic controllers (FLCs). The adaptive schema is divided into two phases: the first phase is concerned with the adaptive learning method for optimizing the MFs parameters based on the binary coded genetic algorithms. The second phase is about the learning and reducing: automatically generate the fuzzy rules and at the same time apply the genetic reduction technique to determine the minimum number of fuzzy rules required in building the fuzzy models. In the rule base, the redundant rules are removed by setting their all consequents weight factor to zero and merging the conflicting rules during the learning process. The real and binary coded coupled genetic algorithms are applied for generating the optimal controllers that reduce the rule base size and optimal selection of fuzzy sets. Optimizing the MFs of FLCs with learning and reducing the number of fuzzy control rules concurrently represents a way to improve the computational efficiency and interpretability of FLCs to minimize the errors. The control algorithm is successfully tested for intelligent control of two degrees of freedom inverted pendulum. Finally, the simulation studies exhibits competing results with high accuracy that demonstrate the effective use of the proposed algorithm.","PeriodicalId":424622,"journal":{"name":"2013 IEEE Symposium on Computational Intelligence in Control and Automation (CICA)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117246647","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

Computational intelligence and low cost sensors in biomass combustion process 生物质燃烧过程中的计算智能和低成本传感器

2013 IEEE Symposium on Computational Intelligence in Control and Automation (CICA) Pub Date : 2013-04-16 DOI: 10.1109/CICA.2013.6611681

Jan Pital, Jozef Mizak

引用次数: 18

Path planning of a data mule in wireless sensor network using an improved implementation of clustering-based genetic algorithm 基于改进的聚类遗传算法的无线传感器网络数据骡子路径规划

2013 IEEE Symposium on Computational Intelligence in Control and Automation (CICA) Pub Date : 2013-04-16 DOI: 10.1109/CICA.2013.6611660

Jing-Sin Liu, Shao-You Wu, Ko-Ming Chiu

{"title":"Path planning of a data mule in wireless sensor network using an improved implementation of clustering-based genetic algorithm","authors":"Jing-Sin Liu, Shao-You Wu, Ko-Ming Chiu","doi":"10.1109/CICA.2013.6611660","DOIUrl":"https://doi.org/10.1109/CICA.2013.6611660","url":null,"abstract":"In recent years, use of mobile robot acting as a data mule for collecting data in the wireless sensor network has become an important issue. This data collection problem of generating a path as short as possible for a data mule to gather all data from all of sensor nodes is known as a NP-hard problem named Traveling Salesman Problem with Neighborhoods (TSPN). We proposed a clustering-based genetic algorithm (CBGA) capable of further shortening the TSPN route provided by clustering with demonstrated effectiveness and reduced computational complexity. In this paper, we seek effective implementation of CBGA by extensive simulations. An improved clustering-based genetic algorithm is proposed, which consists of a waypoint selection method and a GA with an appropriate combination of modified sequential constructive crossover (MSCX) operator and a mutation operator based on local optimization heuristics of 2-opt developed for TSP. Extensive simulations are performed to illustrate the effectiveness and improved performance of CBGA with a more effective GA implementation composed of a combination of MSCX crossover operator and 2-opt for path planning of a data mule.","PeriodicalId":424622,"journal":{"name":"2013 IEEE Symposium on Computational Intelligence in Control and Automation (CICA)","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132629551","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: 30

Intention estimation using time-varying fuzzy Markov models 基于时变模糊马尔可夫模型的意图估计

2013 IEEE Symposium on Computational Intelligence in Control and Automation (CICA) Pub Date : 2013-04-16 DOI: 10.1109/CICA.2013.6611682

Peter Liu, Chang-En Yang

引用次数: 0

Discrete direct adaptive ELM controller for seismically excited non-linear base-isolated buildings 地震激励非线性隔震建筑的离散直接自适应ELM控制器

2013 IEEE Symposium on Computational Intelligence in Control and Automation (CICA) Pub Date : 2013-04-16 DOI: 10.1109/CICA.2013.6611676

R. Subasri, A. Natarajan, S. Sundaram

{"title":"Discrete direct adaptive ELM controller for seismically excited non-linear base-isolated buildings","authors":"R. Subasri, A. Natarajan, S. Sundaram","doi":"10.1109/CICA.2013.6611676","DOIUrl":"https://doi.org/10.1109/CICA.2013.6611676","url":null,"abstract":"Structures with fixed-base will produce high accelerations and inter-storey drifts and move laterally during earthquake. The presence of base isolation devices between ground and the structure, reduces the structural vibrations. To maintain the seismic response of structures within safety, service and comfort limits, the combination of base isolators and feedback controllers have been proposed in recent years. This paper proposes a discrete direct adaptive extreme learning machine (ELM) controller for the active control of non linear base isolated building with hysteretic isolation system. The controller is constructed based on a single hidden layer feed forward network and the parameters of the network are adapted using extreme learning machine (ELM) algorithm. In this work, different from the original ELM algorithm the output weights of the network are updated using Lyapunov stability approach, to guarantee the stability of the structure. The performance of the proposed controller is verified on a non-linear three dimensional benchmark base-isolated structure by exciting the structure with three earthquake samples. The result shows that the proposed controller is effective in reducing the seismic responses of the isolation members as well as the superstructure.","PeriodicalId":424622,"journal":{"name":"2013 IEEE Symposium on Computational Intelligence in Control and Automation (CICA)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130644992","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

Design of adaptive moving-target tracking control for vision-based mobile robot 基于视觉的移动机器人自适应运动目标跟踪控制设计

2013 IEEE Symposium on Computational Intelligence in Control and Automation (CICA) Pub Date : 2013-04-16 DOI: 10.1109/CICA.2013.6611684

You-Wei Lin, R. Wai

引用次数: 3

Enhanced monitoring using PCA-based GLR fault detection and multiscale filtering 基于pca的GLR故障检测和多尺度滤波增强监测

2013 IEEE Symposium on Computational Intelligence in Control and Automation (CICA) Pub Date : 2013-04-16 DOI: 10.1109/CICA.2013.6611656

F. Harrou, M. Nounou, H. Nounou

{"title":"Enhanced monitoring using PCA-based GLR fault detection and multiscale filtering","authors":"F. Harrou, M. Nounou, H. Nounou","doi":"10.1109/CICA.2013.6611656","DOIUrl":"https://doi.org/10.1109/CICA.2013.6611656","url":null,"abstract":"One of the most popular multivariate statistical methods used for data-based process monitoring is Principal Component Analysis (PCA). In the absence of a process model, PCA has been successfully used as a data-based FD technique for highly correlated process variables. Some of the PCA detection indices include the T2 or Q statistics, which have their advantages and disadvantages. When a process model is available, however, the generalized likelihood ratio (GLR) test, which is a statistical hypothesis testing method, has shown good fault detection abili ties. In this work, a PCA-based GLR fault detection algorithm is developed to exploit the advantages of the GLR test in the absence of a process model. In fact, PCA is used to provide a modeling framework for the develop fault detection algorithm. The PCA-based GLR fault detection algorithm provides optimal properties by maximizing the detection probability of faults for a given false alarm rate. However, the presence of measurement noise and modeling errors increase the rate of false alarms. Therefore, to further improve the quality of fault detection, multiscale filtering is utilized to filter the residuals obtained from the PCA model, which helps suppress the effect on errors, and thus decrease the false alarm rate. The proposed fault detection methodology is demonstrated through its application to monitor the ozone level in the Upper Normandy region, France, and it is shown to effectively reduce the rate of false alarms whilst retaining the capability of detecting process faults.","PeriodicalId":424622,"journal":{"name":"2013 IEEE Symposium on Computational Intelligence in Control and Automation (CICA)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122850506","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: 15

Study on distributed internal synchronization in a LAN network 局域网中分布式内部同步的研究

2013 IEEE Symposium on Computational Intelligence in Control and Automation (CICA) Pub Date : 2013-04-16 DOI: 10.1109/CICA.2013.6611685

Lei Yang, Hu Lin, Liaomo Zheng

引用次数: 0

Modeling and analyzing technical systems as complex networks: Detecting inverse response 作为复杂网络的技术系统建模与分析:检测逆响应

2013 IEEE Symposium on Computational Intelligence in Control and Automation (CICA) Pub Date : 2013-04-16 DOI: 10.1109/CICA.2013.6611668

A. Geiger, A. Kroll

{"title":"Modeling and analyzing technical systems as complex networks: Detecting inverse response","authors":"A. Geiger, A. Kroll","doi":"10.1109/CICA.2013.6611668","DOIUrl":"https://doi.org/10.1109/CICA.2013.6611668","url":null,"abstract":"”Complex networks” is the term for a research area where complex systems are modeled by a graph to analyze their structural behavior. They are mostly used in the areas of social sciences, biology and physics. For example, complex networks are a proper method to describe and analyze the non-trivial characteristics depending on the interconnection in a society or between human organs. In the context of computational intelligence, this paper introduces an idea to transfer the methods of the area of complex networks to technical systems, and, fur-thermore, enhance them to permit analyzing dynamic behavior. As a basis for the method transfer a transfer-function-based graph is presented which allows modeling technical systems in the same way as complex networks. The potential to detect dynamical behavior, in addition to structural behavior, is demonstrated by a new algorithm that detects inverse response in interconnected systems based on methods of complex networks. The introduced algorithm provides a qualitative answer if inverse response behavior is possible between a pair of input and output of a system. Finally, two case studies are used to demonstrate the algorithm.","PeriodicalId":424622,"journal":{"name":"2013 IEEE Symposium on Computational Intelligence in Control and Automation (CICA)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132855053","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: 7