2008 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications最新文献_第3页

Artificial immune inspired fault detection algorithm based on fuzzy clustering and genetic algorithm methods 基于模糊聚类和遗传算法的人工免疫激励故障检测算法

2008 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications Pub Date : 2008-07-14 DOI: 10.1109/CIMSA.2008.4595840

I. Aydin, M. Karakose, E. Akin

{"title":"Artificial immune inspired fault detection algorithm based on fuzzy clustering and genetic algorithm methods","authors":"I. Aydin, M. Karakose, E. Akin","doi":"10.1109/CIMSA.2008.4595840","DOIUrl":"https://doi.org/10.1109/CIMSA.2008.4595840","url":null,"abstract":"Early detection and diagnosis of incipient faults are desired for online condition monitoring and improved operational efficiency of induction motors. In this study, an artificial immune inspired fault detection algorithm based on fuzzy clustering and genetic algorithm is developed to detect broken rotor bar and broken connector faults in induction motors. The proposed algorithm uses only one phase stator current as input without the need for any other signals. The new feature signal called envelop is obtained by using Hilbert transform. This signal is examined in a phase space that is constructed by nonlinear time series analysis method. The artificial immune algorithm called negative selection is used to detect faults. The cluster centers of healthy motor phase space are obtained by fuzzy clustering method and they are taken as self patterns. The detectors of negative selection are generated by genetic algorithm. Self patterns generated by fuzzy clustering speed up the training stage of our algorithm and only small numbers of detectors are sufficient to detect any faults of induction motor. Results have demonstrated that the proposed system is able to detect faults in a three phase induction motor, successfully.","PeriodicalId":302812,"journal":{"name":"2008 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134632036","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: 28

Fuzzy flip-flop based neural network as a function approximator 基于模糊触发器的神经网络作为函数逼近器

2008 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications Pub Date : 2008-07-14 DOI: 10.1109/CIMSA.2008.4595830

R. Lovassy, L. Kóczy, L. Gál

引用次数: 3

Neural models for ambient temperature modelling 用于环境温度建模的神经模型

2008 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications Pub Date : 2008-07-14 DOI: 10.1109/CIMSA.2008.4595833

Ceravolo F Di, Pietra B, Pizzuti S, Puglisi G

引用次数: 5

Diagnosis and monitoring of complex industrial processes based on self-organizing maps and watershed transformations 基于自组织图和分水岭转换的复杂工业过程的诊断和监测

2008 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications Pub Date : 2008-07-14 DOI: 10.1109/CIMSA.2008.4595839

Christian Frey

引用次数: 42

On interconnected multi-agent dynamical systems with non-uniform heterogeneous coupling link weights 非均匀异构耦合链路权的互联多智能体动态系统

2008 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications Pub Date : 2008-07-14 DOI: 10.1109/CIMSA.2008.4595846

D. Megherbi, M. Madera, L. Dang

{"title":"On interconnected multi-agent dynamical systems with non-uniform heterogeneous coupling link weights","authors":"D. Megherbi, M. Madera, L. Dang","doi":"10.1109/CIMSA.2008.4595846","DOIUrl":"https://doi.org/10.1109/CIMSA.2008.4595846","url":null,"abstract":"With the increase of terrorism, the ability for law enforcement and government agencies to make intelligent decisions on the fly has become critical. When under attack, it is of utter importance to make timely decisions early enough for the information to reach its destination in time. But how one can know the maximum time by which a decision ought to be made? While little work and still in its infancy has been reported in the literature to address this problem, more work, however, has been done on the controllability and observability of interconnected systems. The approach we take in this paper to address this problem is to study the stability (convergence time) of an interconnected dynamical system (IDS) with respect to its connectivity. We select a class of IDSs as a case study in our analysis. In particular, we show that for the class of IDSs selected the more connected the dynamical systems are, the longer it will take for the overall system to stabilize. We propose a way to analytically derive the convergence time of the system based on its varying interconnections. Finally, the applications of what is proposed is not subject to this problem only, but also to robots/ground/flying vehicles/ formation, neural networks, and any other field where a network of dynamical systems is employed.","PeriodicalId":302812,"journal":{"name":"2008 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126303516","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: 2

Neural technologies for increasing the GPS position accuracy 提高GPS定位精度的神经网络技术

2008 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications Pub Date : 2008-07-14 DOI: 10.1109/CIMSA.2008.4595822

V. D. Lecce, A. Amato, V. Piuri

引用次数: 19

Toward a theory of validation of hybrid MinMax FuzzyNeuro systems 一种混合MinMax模糊神经系统的验证理论

2008 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications Pub Date : 2008-05-02 DOI: 10.1109/CIMSA.2008.4595831

M. Beldjehem

引用次数: 5