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

筛选
英文 中文
ST Segment Change Detection by Means of Wavelets 基于小波的ST段变化检测
2006 8th Seminar on Neural Network Applications in Electrical Engineering Pub Date : 2006-09-01 DOI: 10.1109/NEUREL.2006.341196
N. Milosavljevic, A. Petrovic
{"title":"ST Segment Change Detection by Means of Wavelets","authors":"N. Milosavljevic, A. Petrovic","doi":"10.1109/NEUREL.2006.341196","DOIUrl":"https://doi.org/10.1109/NEUREL.2006.341196","url":null,"abstract":"This research aims to contribute to the automatic interpretation of long sequences of electrocardiograms (ECG) typical for Holter monitoring. We developed a method that uses wavelets for extracting ECG patterns that are characteristic for myocardial ischemia. It was our intention to detect the beats in the simplest possible manner and generate a quantitative estimate of myocardial ischemia likelihood which would suit needs of cardiologists. Biorthogonal wavelets were applied in order to define ST segment properties at different scales. The new method was tested on data from the European ST-T change database. Results show that this method it effective for distinguishing normal from ischemic ECGs. The element that makes the distinction is the correlation of number of ST deviations with the time of consecutive appearances","PeriodicalId":231606,"journal":{"name":"2006 8th Seminar on Neural Network Applications in Electrical Engineering","volume":"54 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":"127260906","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
Reinforcement Learning in Humanoid Robotics Dusko Katic 人形机器人中的强化学习
2006 8th Seminar on Neural Network Applications in Electrical Engineering Pub Date : 2006-09-01 DOI: 10.1109/NEUREL.2006.341182
D. Katic
{"title":"Reinforcement Learning in Humanoid Robotics Dusko Katic","authors":"D. Katic","doi":"10.1109/NEUREL.2006.341182","DOIUrl":"https://doi.org/10.1109/NEUREL.2006.341182","url":null,"abstract":"Summary form only given. Dynamic bipedal walking is difficult to learn because combinatorial explosion in order to optimize performance in every possible configuration of the robot, uncertainties of the robot dynamics that must be only experimentally validated, and because coping with dynamic discontinuities caused by collisions with the ground and with the problem of delayed reward-torques applied at one time may have an effect on the performance many steps into the future. The detailed and precise training data for learning is often hard to obtain or may not be available in the process of biped control synthesis. Since no exact teaching information is available, this is a typical reinforcement learning problem and the failure signal serves as the reinforcement signal. Reinforcement learning (RL) offers one of the most general framework to humanoid robotics towards true autonomy and versatility. Various straightforward and hybrid intelligent control algorithms based RL for active and passive biped locomotion is presented. The proposed reinforcement learning algorithms is based on two different learning structures: actor-critic architecture and Q-learning structures. Also, RL algorithms can use numerical and fuzzy evaluative feedback information for external reinforcement. The proposed RL algorithms use the learning elements that consist of various types of neural networks, fuzzy logic nets or fuzzy-neuro networks with focus on fast convergence properties and small number of learning trials","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":"122565927","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
Data Mining a Prostate Cancer Dataset Using Neural Networks 基于神经网络的前列腺癌数据挖掘
2006 8th Seminar on Neural Network Applications in Electrical Engineering Pub Date : 2006-09-01 DOI: 10.1109/NEUREL.2006.341201
K. Revett
{"title":"Data Mining a Prostate Cancer Dataset Using Neural Networks","authors":"K. Revett","doi":"10.1109/NEUREL.2006.341201","DOIUrl":"https://doi.org/10.1109/NEUREL.2006.341201","url":null,"abstract":"Prostate cancer remains one of the leading causes of cancer death worldwide, with a reported incidence rate of 650,000 cases per annum worldwide. The causal factors of prostate cancer still remain to be determined. In this paper, we investigate a medical dataset containing clinical information on 502 prostate cancer patients using the machine learning technique of rough sets and radial basis function neural network. Our preliminary results yield a classification accuracy of 90%, with high sensitivity and specificity (both at approximately 91%). Our results yield a predictive positive value (PPN) of 81% and a predictive negative value (PNV) of 95%","PeriodicalId":231606,"journal":{"name":"2006 8th Seminar on Neural Network Applications in Electrical Engineering","volume":"5 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":"123904755","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: 3
Percolation approach to formation of synfire chains in two dimensional neural networks 二维神经网络中协同链形成的渗透方法
2006 8th Seminar on Neural Network Applications in Electrical Engineering Pub Date : 2006-09-01 DOI: 10.1109/NEUREL.2006.341178
I. Franović, V. Miljkovic
{"title":"Percolation approach to formation of synfire chains in two dimensional neural networks","authors":"I. Franović, V. Miljkovic","doi":"10.1109/NEUREL.2006.341178","DOIUrl":"https://doi.org/10.1109/NEUREL.2006.341178","url":null,"abstract":"We consider the propagation of spike packets in two dimensional networks consisting of locally coupled neural pools. The dynamic attractors of this model, synfire chains, appear for some values of network parameters. The synfire chain formation exhibits behavior, which may be discribed with the percolation phase transition. Using finite-size scaling method, we obtained the critical probabilities and the critical parameter ratio beta/v for different sets of refractoriness and synaptic weights, connecting neighbouring neural pools","PeriodicalId":231606,"journal":{"name":"2006 8th Seminar on Neural Network Applications in Electrical Engineering","volume":"16 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":"128713247","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
Application of Neural Network for Automatic Classification of Leukocytes 神经网络在白细胞自动分类中的应用
2006 8th Seminar on Neural Network Applications in Electrical Engineering Pub Date : 2006-09-01 DOI: 10.1109/NEUREL.2006.341197
Stanislav Mircic, Nikola Jorgovanovic
{"title":"Application of Neural Network for Automatic Classification of Leukocytes","authors":"Stanislav Mircic, Nikola Jorgovanovic","doi":"10.1109/NEUREL.2006.341197","DOIUrl":"https://doi.org/10.1109/NEUREL.2006.341197","url":null,"abstract":"Differential blood count is one of the most frequently used diagnostic methods in medicine. An algorithm for leukocytes classification represents the crucial part of any device for the automatic compilation of a differential blood count. This paper demonstrates a new algorithm for the automatic classification of leukocytes based on neural networks and digital image processing. The results of the algorithm testing show a high sensitivity of the algorithm in leukocyte detection, as well as classification accuracy of 86%","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":"129275872","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
Dynamic-Order-Extended Time-Delay Dynamic Neural Units 动态有序扩展时滞动态神经单元
2006 8th Seminar on Neural Network Applications in Electrical Engineering Pub Date : 2006-09-01 DOI: 10.1109/NEUREL.2006.341189
I. Bukovský, G. Simeunovic
{"title":"Dynamic-Order-Extended Time-Delay Dynamic Neural Units","authors":"I. Bukovský, G. Simeunovic","doi":"10.1109/NEUREL.2006.341189","DOIUrl":"https://doi.org/10.1109/NEUREL.2006.341189","url":null,"abstract":"The paper introduces a linear dynamic-order-extended time-delay dynamic neural unit, which is one possible modification of novel class of artificial neurons called time-delay dynamic neural units (TmD-DNU). In standalone implementations, these artificial dynamic neural architectures can be understood as an analogy to continuous time-delay differential equations. TmD-DNU is capable of identification of all parameters of continuous time differential equation including unknown time delays both in the unit's inputs as well as in its state variable. A modification of dynamic backpropagation learning algorithm is shown. Results on system identification of an unknown system with dynamics of higher-order including unknown time delays are shown in comparison to achievements by common identification methods applied to the same system. Robust identification capabilities and network implementations of TmD-DNU are briefly discussed","PeriodicalId":231606,"journal":{"name":"2006 8th Seminar on Neural Network Applications in Electrical Engineering","volume":"45 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":"127929953","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: 6
GA-Based Feature Extraction for Clapping Sound Detection 基于ga的拍手声特征提取
2006 8th Seminar on Neural Network Applications in Electrical Engineering Pub Date : 2006-09-01 DOI: 10.1109/NEUREL.2006.341166
J. Olajec, R. Jarina, M. Kuba
{"title":"GA-Based Feature Extraction for Clapping Sound Detection","authors":"J. Olajec, R. Jarina, M. Kuba","doi":"10.1109/NEUREL.2006.341166","DOIUrl":"https://doi.org/10.1109/NEUREL.2006.341166","url":null,"abstract":"Automatically extracting semantic content from audio streams can be helpful in many multimedia applications. In this paper, we introduce a framework for automatic feature subspace selection from a common feature vector. The selected features build a new representation which is better suitable for a given learning task and recognition. In order to solve this problem, we propose the GA-based (genetic algorithm) method to improve the representativeness and robustness of the features generic audio recognition task","PeriodicalId":231606,"journal":{"name":"2006 8th Seminar on Neural Network Applications in Electrical Engineering","volume":"21 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":"126717094","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: 12
Synchronization of Chaotic Cellular Neural Networks based on Rössler Cells 基于Rössler细胞的混沌细胞神经网络同步
2006 8th Seminar on Neural Network Applications in Electrical Engineering Pub Date : 2006-09-01 DOI: 10.1109/NEUREL.2006.341171
D. Rijlaarsdam, V. Mladenov
{"title":"Synchronization of Chaotic Cellular Neural Networks based on Rössler Cells","authors":"D. Rijlaarsdam, V. Mladenov","doi":"10.1109/NEUREL.2006.341171","DOIUrl":"https://doi.org/10.1109/NEUREL.2006.341171","url":null,"abstract":"Using and extending the approach in previous studies we demonstrate synchronization of two hyper chaotic cellular neural networks consisting of 25 cells governed by chaotic Rossler dynamics. We guarantee global asymptotic stability of the synchronization manifold by designing a nonlinear observer in such a way that the resulting error system is linear and time invariant. This linear error system is evaluated and a state feedback is designed to accomplish full state synchronization. Analytical as well as numerical simulation results are presented","PeriodicalId":231606,"journal":{"name":"2006 8th Seminar on Neural Network Applications in Electrical Engineering","volume":"26 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":"121972479","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: 8
Bias-Dependent Model of Microwave FET S-parameters Based on Prior Knowledge ANNs 基于先验知识人工神经网络的微波场效应管s参数偏差相关模型
2006 8th Seminar on Neural Network Applications in Electrical Engineering Pub Date : 2006-09-01 DOI: 10.1109/NEUREL.2006.341208
Z. Marinković, O. Pronic, V. Markovic
{"title":"Bias-Dependent Model of Microwave FET S-parameters Based on Prior Knowledge ANNs","authors":"Z. Marinković, O. Pronic, V. Markovic","doi":"10.1109/NEUREL.2006.341208","DOIUrl":"https://doi.org/10.1109/NEUREL.2006.341208","url":null,"abstract":"The applications of artificial neural networks (ANNs) in bias-dependent modeling of S-parameters of microwave FETs have been proposed earlier. Here, a model based on an ANN with additional prior knowledge at its inputs (PKI ANN) is introduced. S-parameters of the device that belongs to the same class as the modeled device are used as the prior knowledge. The PKI concept allows ANN model to be developed with less training data, which is very advantageous when training data is expensive or time consuming to obtain. The proposed modeling concept is illustrated by an appropriate modeling example","PeriodicalId":231606,"journal":{"name":"2006 8th Seminar on Neural Network Applications in Electrical Engineering","volume":"122 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":"131451711","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: 6
Face Recognition by Using Unitary Vector Spaces 基于酉向量空间的人脸识别
2006 8th Seminar on Neural Network Applications in Electrical Engineering Pub Date : 2006-09-01 DOI: 10.1109/NEUREL.2006.341173
G. Kekovic, D. Raković
{"title":"Face Recognition by Using Unitary Vector Spaces","authors":"G. Kekovic, D. Raković","doi":"10.1109/NEUREL.2006.341173","DOIUrl":"https://doi.org/10.1109/NEUREL.2006.341173","url":null,"abstract":"Dynamic developments of science and technology have demanded necessity of interdisciplinary approach and appearance of novel scientific disciplines. In this respect, face recognition using quantum mechanical methods of unitary vector spaces, represents very interesting field due to possible applications in the field of quantum informatics. Thus traditional quantum mechanical methods widely applied to microsystems during the past century are now successfully extrapolated in macroscopic information framework as well","PeriodicalId":231606,"journal":{"name":"2006 8th Seminar on Neural Network Applications in Electrical Engineering","volume":"21 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":"131057703","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
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信