2016 13th Symposium on Neural Networks and Applications (NEUREL)最新文献

Deep prediction model: The case of online adaptive prediction of subcutaneous glucose 深度预测模型:以在线自适应预测皮下葡萄糖为例

2016 13th Symposium on Neural Networks and Applications (NEUREL) Pub Date : 2016-12-29 DOI: 10.1109/NEUREL.2016.7800095

Marko V. Jankovic, S. Mosimann, L. Bally, C. Stettler, S. Mougiakakou

引用次数: 11

Neural networks application to Neretva basin hydro-meteorological data 神经网络在Neretva流域水文气象资料中的应用

2016 13th Symposium on Neural Networks and Applications (NEUREL) Pub Date : 2016-11-01 DOI: 10.1109/NEUREL.2016.7800126

M. Daković, Tijana Ruzic, Tanja Rogac, M. Brajović, B. Lutovac

引用次数: 2

Modeling of actuation voltage of RF MEMS capacitive switches based on RBF ANNs 基于RBF神经网络的射频MEMS电容开关驱动电压建模

2016 13th Symposium on Neural Networks and Applications (NEUREL) Pub Date : 2016-11-01 DOI: 10.1109/NEUREL.2016.7800133

T. Ćirić, Z. Marinković, O. Pronić-Rančić, V. Markovic, L. Vietzorreck

{"title":"Modeling of actuation voltage of RF MEMS capacitive switches based on RBF ANNs","authors":"T. Ćirić, Z. Marinković, O. Pronić-Rančić, V. Markovic, L. Vietzorreck","doi":"10.1109/NEUREL.2016.7800133","DOIUrl":"https://doi.org/10.1109/NEUREL.2016.7800133","url":null,"abstract":"Artificial neural networks (ANNs) have been exploited as an efficient tool in modeling of many electronic devices, among them RF MEMS devices. The design of RF MEMS devices requires determination of their electrical and mechanical characteristics according to the application requirements. ANNs have been proposed to be used for modeling RF MEMS devices and can be used further as an alternative and efficient simulation and optimization tool replacing time consuming simulations in standard electrical and mechanical simulators. The aim of this paper is to investigate possibilities of the radial basis function (RBF) ANNs to be applied for modeling of mechanical characteristics of RF MEMS capacitive switches, relating the switch geometry parameters and the actuation voltage. The achieved results obtained by the developed RBF neural model are compared with the results from the earlier developed multilayer perceptron (MLP) neural model. Moreover, effectiveness and accuracy of these two ANN models are analysed. The results confirm the efficiency of the both modelling approaches.","PeriodicalId":331222,"journal":{"name":"2016 13th Symposium on Neural Networks and Applications (NEUREL)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129890167","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

Analysis of learned features for remote sensing image classification 基于学习特征的遥感图像分类分析

2016 13th Symposium on Neural Networks and Applications (NEUREL) Pub Date : 2016-11-01 DOI: 10.1109/NEUREL.2016.7800145

V. Risojevic

引用次数: 5

Sparse signal recovery based on concentration measures and genetic algorithm 基于浓度测度和遗传算法的稀疏信号恢复

2016 13th Symposium on Neural Networks and Applications (NEUREL) Pub Date : 2016-11-01 DOI: 10.1109/NEUREL.2016.7800115

M. Brajović, B. Lutovac, I. Orović, M. Daković, S. Stankovic

引用次数: 6

Mathematical models for bone density assessment 骨密度评估的数学模型

2016 13th Symposium on Neural Networks and Applications (NEUREL) Pub Date : 2016-11-01 DOI: 10.1109/NEUREL.2016.7800102

V. Zeljkovic, I. Vucenik, P. Mayorga, J. Valdez, C. Tameze, J. Stains, C. Druzgalski

引用次数: 5

Performance metrics for personalized program guides 个性化节目指南的性能指标

2016 13th Symposium on Neural Networks and Applications (NEUREL) Pub Date : 2016-11-01 DOI: 10.1109/NEUREL.2016.7800131

M. Krstić, M. Bjelica

{"title":"Performance metrics for personalized program guides","authors":"M. Krstić, M. Bjelica","doi":"10.1109/NEUREL.2016.7800131","DOIUrl":"https://doi.org/10.1109/NEUREL.2016.7800131","url":null,"abstract":"The performance of TV recommender system which uses machine learning techniques is degraded due to imbalanced distribution of collected viewing preferences. As users have a tendency to provide positive feedbacks much more than the negative ones, the system that does not use methods to deal with class imbalance provides poor prediction of the contents that the user does not like to watch. Thus undesirable contents can often be recommended, which is perceived by users as bad recommendation. The probability of bad recommendations can be significantly decreased if the information about class imbalance is incorporated into the machine learning algorithm. However, this improvement comes at expense of degraded prediction of contents that user likes to watch; thus the quality of recommendations is decreased. In addition to learning algorithm, the choice of performance metric that is maximized influences user perception of the system performance. In this paper, it is shown that using the adjusted G-mean instead of G-mean metric can increase the quality of recommendations provided by the system based on neural network, without significant increase in the probability of bad recommendations. This further results in the increase of the recommendation diversity and, consequently, in the increase of user satisfaction.","PeriodicalId":331222,"journal":{"name":"2016 13th Symposium on Neural Networks and Applications (NEUREL)","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127211567","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

Hand gesture recognition using neural network based techniques 基于神经网络的手势识别技术

2016 13th Symposium on Neural Networks and Applications (NEUREL) Pub Date : 2016-11-01 DOI: 10.1109/NEUREL.2016.7800104

Vladislava N. Bobic, P. Tadić, G. Kvascev

引用次数: 23

Solving fuzzy linear systems with EP matrix using a block representation of generalized inverses 用广义逆的分块表示求解带有EP矩阵的模糊线性系统

2016 13th Symposium on Neural Networks and Applications (NEUREL) Pub Date : 2016-11-01 DOI: 10.1109/NEUREL.2016.7800112

Vera Miler-Jerković, M. Janković, Branko J. Malesevic, B. Mihailovic

引用次数: 1

Noise reduction by using autoassociative neural networks 基于自关联神经网络的降噪方法

2016 13th Symposium on Neural Networks and Applications (NEUREL) Pub Date : 2016-11-01 DOI: 10.1109/NEUREL.2016.7800117

Nataša Vlahović, G. Kvascev

{"title":"Noise reduction by using autoassociative neural networks","authors":"Nataša Vlahović, G. Kvascev","doi":"10.1109/NEUREL.2016.7800117","DOIUrl":"https://doi.org/10.1109/NEUREL.2016.7800117","url":null,"abstract":"Noise reduction has always been an important part of any control, acquisition or processing task. In order to increase the usage of some smaller and cheaper, but on the other hand less precise sensor solutions, it is necessary to incorporate some signal processing techniques for noise reduction. Nowadays soft computing techniques such as neural networks are widely used in many signal processing applications and provide very good results. In this paper, an approach to noise reduction by using autoassociative neural networks is described. The main idea is to use more precise, therefore more expensive sensor, for the network training, and afterward use this network for less precise and cheaper sensor signal processing. So, when the network is formed, it is possible to use less precise sensor and use the network for noise reduction. This would ensure noise reduction in the less precise sensor signal. With this signal processing tool, less precise sensors could be used in desired applications. When comparing the results obtained by using autoassociative neural networks with results obtained by using digital filters, the obvious advantage is that neural networks do not bring delay into the system like filters do. All described simulations and data processing are performed in Matlab and Simulink.","PeriodicalId":331222,"journal":{"name":"2016 13th Symposium on Neural Networks and Applications (NEUREL)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116150080","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