2006 8th Seminar on Neural Network Applications in Electrical Engineering最新文献

{"title":"Fault Diagnosis in Digital Part of Sigma-Delta Converter","authors":"M. Andrejević, V. Litovski","doi":"10.1109/NEUREL.2006.341206","DOIUrl":"https://doi.org/10.1109/NEUREL.2006.341206","url":null,"abstract":"In this paper the artificial neural network (ANN) is applied to diagnosis of defects in the digital part of a nonlinear mixed-mode circuit. Both catastrophic and delay defects are considered. The approach is demonstrated on the example of a relatively complex sigma-delta modulator. Delay defects in this example are delays of rising and falling edge of digital signals and catastrophic defects are considered as stuck switches. Fault dictionary is created, by simulation, using the response of the circuit to an input ramp signal. It is represented in a form of a look-up table. Artificial neural network is then trained for modeling (memorizing) the look-up table. The diagnosis is performed so that the ANN is excited by faulty responses in order to present the fault codes at its output. There were no errors in identifying the faults during diagnosis","PeriodicalId":231606,"journal":{"name":"2006 8th Seminar on Neural Network Applications in Electrical Engineering","volume":"144 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116441503","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}

{"title":"Chaos Based Analog-to-digital Conversion of Small Signals","authors":"V. Litovski, M. Andrejević, M. Nikolic","doi":"10.1109/NEUREL.2006.341205","DOIUrl":"https://doi.org/10.1109/NEUREL.2006.341205","url":null,"abstract":"In this paper a typical one-dimensional chaotic system, a tent map, can be introduced. This tent map implements A/D conversion function as well as small signal amplifying function. We can show how this system can be applied to analog-to-digital conversion of different types of signals. In order to get full characterization, we made three experiments with the circuit. We performed A/D conversion of 1) a small DC signal, 2) a sine wave, and finally 3) a ramp signal. A correction scheme, taken from literature, was applied in order to get better accuracy. In addition, simulations were performed both with ideal and realistic models of operational amplifiers so getting better information on the circuit behavior","PeriodicalId":231606,"journal":{"name":"2006 8th Seminar on Neural Network Applications in Electrical Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131256304","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}

{"title":"Hybrid Empirical-Neural Model of Microwave Slotted Patch Antennas","authors":"B. Milovanovic, Z. Stanković, M. Milijić","doi":"10.1109/NEUREL.2006.341207","DOIUrl":"https://doi.org/10.1109/NEUREL.2006.341207","url":null,"abstract":"This paper presents a new hybrid empirical-neural model (HEN) for slotted patch antenna modeling. Unlike the model based on a classical multi-layer perceptron (MLP) network, the proposed HEN model includes an existing partial knowledge about both the resonant frequency fr behavior and the minimum value of S11 parameter (s11min) behavior of patch antenna, yielding more accurate determination of these patch antenna parameters","PeriodicalId":231606,"journal":{"name":"2006 8th Seminar on Neural Network Applications in Electrical Engineering","volume":"165 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134129803","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}

{"title":"Nikola Tesla in History of Wireless; Plenary Lecture","authors":"A. Marinčić","doi":"10.1109/NEUREL.2006.341161","DOIUrl":"https://doi.org/10.1109/NEUREL.2006.341161","url":null,"abstract":"","PeriodicalId":231606,"journal":{"name":"2006 8th Seminar on Neural Network Applications in Electrical Engineering","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132044217","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}

{"title":"Phase and Vector Analysis of H5N1 Avian Influenza Virus","authors":"P. Cristea","doi":"10.1109/NEUREL.2006.341191","DOIUrl":"https://doi.org/10.1109/NEUREL.2006.341191","url":null,"abstract":"Conversion of nucleotide sequences into digital signals reveals surprising regularities in the distribution of nucleotides and pairs of nucleotides, in both prokaryotes and eukaryotes. Structurally, a genome appears to be less of a \"plain text\" expressing some content in accordance to certain semantics and grammar rules, but more of a \"poem\", obeying rules of symmetry similar to \"rhythm\" and \"rhyme\". Recurrent patterns in nucleotide sequences are reflected in simple mathematical regularities satisfied by the genomic signals. This approach allows to reveal patterns and restrictions in pathogen variability and to monitor the development of drug resistance. The paper presents some results for the H5N1 Avian influenza virus.","PeriodicalId":231606,"journal":{"name":"2006 8th Seminar on Neural Network Applications in Electrical Engineering","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121677771","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}

{"title":"Data Mining, Neural Networks and Rule Extraction; IEEE CI Distinguish Lecture","authors":"J. Zurada, Samuel T. Fife","doi":"10.1109/NEUREL.2006.341159","DOIUrl":"https://doi.org/10.1109/NEUREL.2006.341159","url":null,"abstract":"This lecture was held during the IEEE SCG CI Chapter meeting. The lecture was open to all members of IEEE. This event is sponsored by Computational Intelligence Society under its Distinguished Lecturer Program. SUMMARY: The opening part of the talk introduces basic premises of data mining. It is shown how numerous paradigms of neurocomputing that are data-driven modeling, feature extraction, dimensionality reduction, visualization, knowledge extraction and logic rule discovery prove useful and effective for data mining. Such modeling, however, often involves handling of heterogenous, subjective, imprecise and noisy data. The second part of the presentation outlines the concept of dimensionality reduction of input data vectors. This technique leads to reduced models achieved through evaluation of sensitivity matrices of perceptron networks. When developing reduced models it is also useful to eliminate underutilized internal weights and also neurons via pruning techniques. The concluding part of the talk reviews the capabilities of perceptron networks for producing understandable IF-THEN rules. Logic rule extraction via neural networks evaluation is discussed and illustrated with examples. SPEAKER: Dr. Jacek M. Zurada is the S.T. Fife Alumni Professor of Electrical and Computer Engineering at the University of Louisville, Louisville, Kentucky, USA. He is the author of the 1992 PWS text Introduction to Artificial Neural Systems, co-editor of the 1994 IEEE Press volume Computational Intelligence. Imitating Life, and of the 2000 MIT Press book Knowledge Based Neurocomputing. He is also the author or co-author of more than 270 journal and conference papers in the area of neural networks, computational intelligence, and data analysis. Dr. Zurada has received a number of awards for distinction in research and teaching, including the 1993 Presidential Award for Research, Scholarship and Creative Activity. In 1998-2003 Dr. Zurada was the Editor-in-Chief of IEEE Transactions on Neural Networks. In 2004-05 he served as the IEEE Computational Intelligence Society President. He is an IEEE Fellow and NNS Distinguished Speaker.","PeriodicalId":231606,"journal":{"name":"2006 8th Seminar on Neural Network Applications in Electrical Engineering","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129618973","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}

{"title":"An approach to Synthesis and Analysis of Complex Recurrent Neural Network","authors":"K. Nikolic, B. Abramović, I. Šćepanović","doi":"10.1109/NEUREL.2006.341180","DOIUrl":"https://doi.org/10.1109/NEUREL.2006.341180","url":null,"abstract":"This paper presents an approach to optimization of recurrent artificial neural networks (RNN) that leans on the appliance of stochastic search (SS). Favor algorithm SS with information accumulation (SSAI) is simple in numerical sense, and does not require a lot of computing time in the optimization process i.e. RNN training, and gives suboptimal results in comparison to gradient methods. In certain sense, suggested approach more appropriate for engineering practice than back propagation error (BPE) method, because it does not condition the differentiability of activation neuron function, as well as transformation of RNN in corresponding multi-layered network with forward propagation signal, and after that gave the problem with a great deal of dimensions. Behind the corresponding theoretical analysis, SSAI is applied on optimization of structure and RNN parameters (supervised learning algorithm), for creation of predictive model which serves for content of useful component in input raw material in technological process of flotation in real time","PeriodicalId":231606,"journal":{"name":"2006 8th Seminar on Neural Network Applications in Electrical Engineering","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131171408","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}

{"title":"Preliminary comparison of different neural-fuzzy mappers for load curve short term prediction","authors":"D. Malkocevic, T. Konjic, Vladimiro Miranda","doi":"10.1109/NEUREL.2006.341216","DOIUrl":"https://doi.org/10.1109/NEUREL.2006.341216","url":null,"abstract":"This paper is written with the didactic purpose of exploring and indicating possibilities to power companies in the Balkan region for the application of adaptive neuro-fuzzy inference system (ANFIS) models in load prediction with real load data set. ANFIS models were trained and tested using 15-minute load data collected in Portugal by the electric power company EDP during a 42 day period. Simulation results gave promising results especially considering small size of used data set. Although the objective of the paper is to demonstrate possibilities for practical implementation, further research and improvement including the contributions of similar approaches in the world must be done","PeriodicalId":231606,"journal":{"name":"2006 8th Seminar on Neural Network Applications in Electrical Engineering","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130607825","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}

{"title":"Dyadic Wavelets for Real-time Heart Rate Monitoring","authors":"D. Bojanic, R. Petrovic, N. Jorgovanovic, D. Popović","doi":"10.1109/NEUREL.2006.341195","DOIUrl":"https://doi.org/10.1109/NEUREL.2006.341195","url":null,"abstract":"We developed the new intelligent virtual ECG device by integrating the dyadic wavelet (DyWT) based algorithm for QRS complex detection into the virtual teleECG. The new virtual instrument (VI) was realized by using LabVIEW software. The system allows real-time detection of the heart rhythm, offline analysis of the previously recorded signals or offline analysis when using the system via Internet. The new system allows the physician to locate and recognize life threatening events in ECG recordings and provides the patient with an ECG alarm system. The tests on data from a MIT-BIH database show that the DyWT based detector detects accurately 99.53% of QRS complexes, and similarly better than 99% for the clinical recordings. The analysis in clinical environment showed that in ECG signals comprising a sharp and large T wave the algorithm must be fine tuned, otherwise it could result with classifying the T wave as the R peak","PeriodicalId":231606,"journal":{"name":"2006 8th Seminar on Neural Network Applications in Electrical Engineering","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129754053","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}

{"title":"Robust Adaptive Control for Complex Systems Employing ANN Emulation of Nonlinear Functions","authors":"G. Dimirovski, Yuanwei Jing, Yanxin Zhang, M. Vukobratovic","doi":"10.1109/NEUREL.2006.341184","DOIUrl":"https://doi.org/10.1109/NEUREL.2006.341184","url":null,"abstract":"A new robust adaptive control design synthesis, which employs both high-order neural networks and math-analytical results, for a class of complex nonlinear mechatronic systems possessing similarity property has been derived. This approach makes an adequate use of the structural feature of composite similarity systems and neural networks to resolve the representation issue of uncertainty interconnections and subsystem gains by on-line updating the weights. This synthesis does guarantee the real stability in closed-loop but requires skills to obtain larger attraction domains. Mechatronic example of an axis-tray drive system, possessing uncertainties, is used to illustrate the proposed technique","PeriodicalId":231606,"journal":{"name":"2006 8th Seminar on Neural Network Applications in Electrical Engineering","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114375878","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}