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11th Symposium on Neural Network Applications in Electrical Engineering最新文献

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Error minimization in Phase-Based Neurons 基于相位神经元的误差最小化
11th Symposium on Neural Network Applications in Electrical Engineering Pub Date : 2012-09-01 DOI: 10.1109/NEUREL.2012.6419996
I. Pavaloiu, P. Cristea
{"title":"Error minimization in Phase-Based Neurons","authors":"I. Pavaloiu, P. Cristea","doi":"10.1109/NEUREL.2012.6419996","DOIUrl":"https://doi.org/10.1109/NEUREL.2012.6419996","url":null,"abstract":"Complex-Valued Neural Networks are extensions of the classical Neural Networks. They have complex-valued weights, accept complex inputs and have more computational power than the classical ones. We discuss in this paper the training for Phase-Based Neurons, neural processing elements similar to Universal Binary Neurons, that uses as weights and bias complex numbers with unit magnitude, the phase being the only tunable parameter.","PeriodicalId":343718,"journal":{"name":"11th Symposium on Neural Network Applications in Electrical Engineering","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125528838","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
Neural approaches for parameter extraction of microwave transistor noise models 微波晶体管噪声模型参数提取的神经网络方法
11th Symposium on Neural Network Applications in Electrical Engineering Pub Date : 2012-09-01 DOI: 10.1109/NEUREL.2012.6419956
Z. Marinković, N. Ivkovic, O. Pronić-Rančić, V. Markovic, A. Caddemi
{"title":"Neural approaches for parameter extraction of microwave transistor noise models","authors":"Z. Marinković, N. Ivkovic, O. Pronić-Rančić, V. Markovic, A. Caddemi","doi":"10.1109/NEUREL.2012.6419956","DOIUrl":"https://doi.org/10.1109/NEUREL.2012.6419956","url":null,"abstract":"The aim of this paper is to analyze and compare two artificial neural network based approaches for parameter extractions of microwave transistor equivalent circuits including noise. In the first approach equivalent circuit parameters are determined from the operating conditions, whereas in the second approach equivalent circuit parameters are determined directly from the measured scattering and noise parameters. In both approaches, multilayer perceptron artificial neural networks are applied. The considered extraction approaches are analyzed on an example of temperature dependent modeling of a pHEMT transistor.","PeriodicalId":343718,"journal":{"name":"11th Symposium on Neural Network Applications in Electrical Engineering","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122873573","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: 1
Radial basis function network based feature extraction for improvement the procedure of sourcing neolithic ceramics 基于径向基函数网络的特征提取改进新石器时代陶瓷采购流程
11th Symposium on Neural Network Applications in Electrical Engineering Pub Date : 2012-09-01 DOI: 10.1109/NEUREL.2012.6419973
G. Kvascev, M. Kvaščev, Z. Djurovic
{"title":"Radial basis function network based feature extraction for improvement the procedure of sourcing neolithic ceramics","authors":"G. Kvascev, M. Kvaščev, Z. Djurovic","doi":"10.1109/NEUREL.2012.6419973","DOIUrl":"https://doi.org/10.1109/NEUREL.2012.6419973","url":null,"abstract":"Modern studies of a cultural heritage objects are increasingly multidisciplinary. A variety of analytical techniques supported by pattern recognition methods can help in answering about the origin, dating or authenticity. Results of sourcing ceramics from three Neolithic sites in Serbia are shown in this paper. The procedure based on radial basic function networks (RBFN) was employed in ceramic characterization. The results obtained were tested in previously established procedure for origin of production determination.","PeriodicalId":343718,"journal":{"name":"11th Symposium on Neural Network Applications in Electrical Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130675775","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
Neural network based optical character recognition system 基于神经网络的光学字符识别系统
11th Symposium on Neural Network Applications in Electrical Engineering Pub Date : 2012-09-01 DOI: 10.1109/NEUREL.2012.6419976
N. Dojčinović, I. Mihajlovic, J. Joković, V. Markovic, B. Milovanovic
{"title":"Neural network based optical character recognition system","authors":"N. Dojčinović, I. Mihajlovic, J. Joković, V. Markovic, B. Milovanovic","doi":"10.1109/NEUREL.2012.6419976","DOIUrl":"https://doi.org/10.1109/NEUREL.2012.6419976","url":null,"abstract":"This paper presents an application of a neural network in the optical character recognition (OCR) system. It introduces general architecture of modern OCR systems, discussing each module in detail. Specific contribution of this paper is novelty of the character extraction and segmentation, by considering them as image features. MSER (Maximally Stable Extremal Regions) feature detector is applied, discussing numerical and practical restrictions for character segmentation and recognition. The neural network is trained for character recognition and applied on the appropriate example.","PeriodicalId":343718,"journal":{"name":"11th Symposium on Neural Network Applications in Electrical Engineering","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125450505","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
Modified SOM based intelligent semi-autonomous navigation system 改进的基于SOM的智能半自主导航系统
11th Symposium on Neural Network Applications in Electrical Engineering Pub Date : 2012-09-01 DOI: 10.1109/NEUREL.2012.6419954
Sammer-Ul Hassan, Bilal Ahmed, Bilal Shaukat, Habib ur Rehman Paracha, U. Malik, Saad Qaiser, Nabeel Anwar, Y. Ayaz
{"title":"Modified SOM based intelligent semi-autonomous navigation system","authors":"Sammer-Ul Hassan, Bilal Ahmed, Bilal Shaukat, Habib ur Rehman Paracha, U. Malik, Saad Qaiser, Nabeel Anwar, Y. Ayaz","doi":"10.1109/NEUREL.2012.6419954","DOIUrl":"https://doi.org/10.1109/NEUREL.2012.6419954","url":null,"abstract":"The fundamental problem in today's world when robots are becoming a part of daily routine is getting them to move from one desired place to another without any problem and collision. In doing so a robot needs to perceive its environment to efficiently navigate through it. The navigation is possible only if the robot differentiates between different scenarios around it in real time. In this paper we present a semi-autonomous navigation strategy designed for low throughput interfaces. The task is to minimize the user input by intelligently interpreting and executing the commands. Analyzing the environment in real time, the robot selects the most probable action using collision free steering techniques which employ the Self Organizing Map in cooperation with Artificial Potential Field, in which both the user and an intelligent robot co-exist. The performance of the proposed navigation algorithm is confirmed with computer simulations and experiments using the Pioneer 3-AT mobile robot.","PeriodicalId":343718,"journal":{"name":"11th Symposium on Neural Network Applications in Electrical Engineering","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132037764","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
Dynamic neural control for maximum power point tracking of PV system 光伏系统最大功率点跟踪的动态神经控制
11th Symposium on Neural Network Applications in Electrical Engineering Pub Date : 2012-09-01 DOI: 10.1109/NEUREL.2012.6420029
A. Dounis, P. Kofinas, C. Alafodimos, D. Tseles
{"title":"Dynamic neural control for maximum power point tracking of PV system","authors":"A. Dounis, P. Kofinas, C. Alafodimos, D. Tseles","doi":"10.1109/NEUREL.2012.6420029","DOIUrl":"https://doi.org/10.1109/NEUREL.2012.6420029","url":null,"abstract":"Development of an effective maximum power point tracking (MPPT) algorithm is important in order to achieve maximum power point in a photovoltaic system (PV). In this study, a dynamic neural control (DNC) scheme is developed. The adaptation procedure is based on the back propagation learning law and is required only a priori knowledge, that's, the system output error. The feasibility of the proposed neural control is evaluated by the simulation results and compared to the conventional perturbation and observation (P&O) method.","PeriodicalId":343718,"journal":{"name":"11th Symposium on Neural Network Applications in Electrical Engineering","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128431414","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 network based single image super resolution 基于神经网络的单幅图像超分辨率
11th Symposium on Neural Network Applications in Electrical Engineering Pub Date : 2012-09-01 DOI: 10.1109/NEUREL.2012.6420014
N. Kumar, P. Deswal, J. Mehta, A. Sethi
{"title":"Neural network based single image super resolution","authors":"N. Kumar, P. Deswal, J. Mehta, A. Sethi","doi":"10.1109/NEUREL.2012.6420014","DOIUrl":"https://doi.org/10.1109/NEUREL.2012.6420014","url":null,"abstract":"In this paper a novel learning based technique for single image super resolution (SR) is proposed. We model the relationship between available low resolution (LR) image and desired high resolution (HR) image as multi-scale markov random field (MSMRF). We re-formulate the SR problem in terms of learning the mapping between LR-MRF and HR-MRF, which is generally non-linear. Instead of learning MSMRF parameters we use artificial neural networks to learn the desired mapping. The results compare favorably to more complex stat-of-the art techniques for 2 × 2 and 3 × 3 SR problem. We solve the SR problem using optical zoom as a cue by the proposed algorithm as well. The results on experiments with real data are presented.","PeriodicalId":343718,"journal":{"name":"11th Symposium on Neural Network Applications in Electrical Engineering","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125538546","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
Empirical ANN models for 2D direction of arrival estimation 二维到达方向估计的经验神经网络模型
11th Symposium on Neural Network Applications in Electrical Engineering Pub Date : 2012-09-01 DOI: 10.1109/NEUREL.2012.6419951
M. Agatonovic, Z. Stanković, B. Milovanovic, L. Sit, T. Zwick
{"title":"Empirical ANN models for 2D direction of arrival estimation","authors":"M. Agatonovic, Z. Stanković, B. Milovanovic, L. Sit, T. Zwick","doi":"10.1109/NEUREL.2012.6419951","DOIUrl":"https://doi.org/10.1109/NEUREL.2012.6419951","url":null,"abstract":"Empirical Artificial Neural Network (ANN) models are developed for Two-Dimensional Direction of Arrival (2D DOA) estimation of a source signal. For that purpose, experimental data obtained from measurements in an anechoic chamber are utilized. Performance of ANN models are compared to 2D MUSIC algorithm in regard to estimation accuracy and speed of calculations. It is demonstrated that the proposed models outperform MUSIC in cases when small number of snapshots are utilized for DOA estimation and at the same time, are more suitable for real-time implementation.","PeriodicalId":343718,"journal":{"name":"11th Symposium on Neural Network Applications in Electrical Engineering","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121103032","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
Connectionist-genetic based algorithm for positioning industrial manipulator 基于连接算子的工业机械臂定位算法
11th Symposium on Neural Network Applications in Electrical Engineering Pub Date : 2012-09-01 DOI: 10.1109/NEUREL.2012.6419964
M. Tomic, Branko Miloradovic, M. Jankovic
{"title":"Connectionist-genetic based algorithm for positioning industrial manipulator","authors":"M. Tomic, Branko Miloradovic, M. Jankovic","doi":"10.1109/NEUREL.2012.6419964","DOIUrl":"https://doi.org/10.1109/NEUREL.2012.6419964","url":null,"abstract":"In this paper the solution of inverse kinematics problem and positioning of the industrial manipulator (ROBED03) with five degrees of freedom are presented. The algorithm is based on combination of Artificial Neural Networks (ANN) and Genetic Algorithm (GA).ANN was used for rough positioning providing the inputs for GA which performs precise adjustment. The algorithm was successfully tested in robot's working space.","PeriodicalId":343718,"journal":{"name":"11th Symposium on Neural Network Applications in Electrical Engineering","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121456990","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
Combined controller architecture for leader-follower robot formation control leader-follower机器人编队控制的组合控制器体系结构
11th Symposium on Neural Network Applications in Electrical Engineering Pub Date : 2012-09-01 DOI: 10.1109/NEUREL.2012.6419962
A. Cosic, M. Susic, S. Graovac, D. Katic
{"title":"Combined controller architecture for leader-follower robot formation control","authors":"A. Cosic, M. Susic, S. Graovac, D. Katic","doi":"10.1109/NEUREL.2012.6419962","DOIUrl":"https://doi.org/10.1109/NEUREL.2012.6419962","url":null,"abstract":"Formation control is an important field in multi-robot coordinated control. Solution of formation navigation in structured static environment is presented in this paper. It is assumed that high level planner is available, which generates collision free trajectory for leader robot. Leader robot is forced to track generated trajectory, while followers' trajectories are generated based on the trajectory realized by the real leader. Real environments contain large number of obstacles, which can be arbitrarily positioned. Hence, formation switching becomes necessary in cases when followers can collide with obstacles. In order to ensure trajectory tracking, as well as object avoidance, control structure with several conventional and fuzzy controllers of different roles (trajectory tracking, obstacle avoiding, vehicle avoiding and combined controller) has been adopted. Kinematic model of differentially driven two-wheeled mobile robot is assumed. Simulation results show the efficiency of the proposed approach.","PeriodicalId":343718,"journal":{"name":"11th Symposium on Neural Network Applications in Electrical Engineering","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122525581","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
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