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Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.最新文献

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An incremental growing neural gas learns topologies 增量增长的神经气体学习拓扑结构
Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005. Pub Date : 2005-12-27 DOI: 10.1109/IJCNN.2005.1556026
Y. Prudent, A. Ennaji
{"title":"An incremental growing neural gas learns topologies","authors":"Y. Prudent, A. Ennaji","doi":"10.1109/IJCNN.2005.1556026","DOIUrl":"https://doi.org/10.1109/IJCNN.2005.1556026","url":null,"abstract":"An incremental and growing network model is introduced which is able to learn the topological relations in a given set of input vectors by means of a simple Hebb-like learning rule. We propose a new algorithm for a SOM which can learn new input data (plasticity) without degrading the previously trained network and forgetting the old input data (stability). We report the validation of this model on experiments using a synthetic problem, the IRIS database and the handwriting digit recognition problem over a portion of the NIST database. Finally we show how to use this network for clustering and semi-supervised clustering.","PeriodicalId":365690,"journal":{"name":"Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130120252","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: 103
Post nonlinear blind source separation by geometric linearization 基于几何线性化的后非线性盲源分离
Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005. Pub Date : 2005-12-27 DOI: 10.1109/IJCNN.2005.1555837
T. Nguyen, J. Patra, A. Das, G. Ng
{"title":"Post nonlinear blind source separation by geometric linearization","authors":"T. Nguyen, J. Patra, A. Das, G. Ng","doi":"10.1109/IJCNN.2005.1555837","DOIUrl":"https://doi.org/10.1109/IJCNN.2005.1555837","url":null,"abstract":"We present a novel geometric approach to the popular post nonlinear (PNL) BSS problem. A PNL mixing system includes two stages: a linear mixing followed by a nonlinear transformation. In our method, the process to linearize the nonlinear observed signals, the most critical task in PNL model, is carried out by a geometric transformation. The basic idea is that in a multi-dimensional space, a PNL mixture is represented by a nonlinear surface while a linear mixture is represented by a plane. Thus, by transforming a PNL's representing nonlinear surface to a plane, the PNL mixture can be linearized. The hidden sources are then estimated from linearized signals by a linear BSS algorithm. Experiments show promising performance of our approach.","PeriodicalId":365690,"journal":{"name":"Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134369497","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 network and principal component analyses of highly variable myocardial mechanical waveforms derived from echocardiographic ultrasound images 由超声心动图超声图像衍生的高度可变心肌机械波形的神经网络和主成分分析
Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005. Pub Date : 2005-12-27 DOI: 10.1109/IJCNN.2005.1556406
McMahon Em, J. Korinek, Honghai Zhang, M. Sonka, A. Manduca, M. Belohlavek
{"title":"Neural network and principal component analyses of highly variable myocardial mechanical waveforms derived from echocardiographic ultrasound images","authors":"McMahon Em, J. Korinek, Honghai Zhang, M. Sonka, A. Manduca, M. Belohlavek","doi":"10.1109/IJCNN.2005.1556406","DOIUrl":"https://doi.org/10.1109/IJCNN.2005.1556406","url":null,"abstract":"We introduce a new type of data for classification of regional segments of myocardium. We have analyzed strain measurements taken throughout the cardiac cycle from the echocardiograms of pigs. Classifications by both principal component analysis (PCA) and by neural network (NN) are combined for a data mining operation. Differences in strain waveforms between normal and diseased myocardium may further elucidate the corresponding changes in physiology. Altered functioning of the heart muscle is reflected by strain, and objective computer analysis should aid in the diagnosis of ischemia. We hypothesize that the entire strain waveform over one heart cycle can be classified to functionally determine whether or not a myocardial region is perfused.","PeriodicalId":365690,"journal":{"name":"Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131602624","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
Associative neural networks as means for low-resolution video-based recognition 基于联想神经网络的低分辨率视频识别方法
Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005. Pub Date : 2005-12-27 DOI: 10.1109/IJCNN.2005.1556419
Dmitry O. Gorodnichy
{"title":"Associative neural networks as means for low-resolution video-based recognition","authors":"Dmitry O. Gorodnichy","doi":"10.1109/IJCNN.2005.1556419","DOIUrl":"https://doi.org/10.1109/IJCNN.2005.1556419","url":null,"abstract":"Techniques developed for recognition of objects in photographs often fail when applied to recognition of the same objects in video. A critical example of such a situation is seen in face recognition, where many technologies are already intensively used for passport verification and where there is no technology that can reliably identify a person from a surveillance video. The reason for this is that video provides images of much lower quality and resolution than that of photographs. Besides, objects in video are normally captured in unconstrained environments, often under poor lighting, in motion and at a distance. This makes memorization of an object from a single video frame unreliable and recognition based on a single video frame very difficult if even possible. This paper introduces a neuro-associative approach to recognition which can both learn and identify an object from low-resolution low-quality video sequences. This approach is derived from a mathematical model of biological visual memory, in which correlation-based projection learning is used to memorize a face from a video sequence and attractor-based association is performed to recognize a face over several video frames. The approach is demonstrated using a video-based facial database and real-time video annotation of TV shows.","PeriodicalId":365690,"journal":{"name":"Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117327153","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: 26
A fast neural network-based detection and tracking of dim moving targets in FLIR imagery 基于神经网络的前视红外图像中弱小运动目标的快速检测与跟踪
Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005. Pub Date : 2005-12-27 DOI: 10.1109/IJCNN.2005.1556430
J. Patra, F. Widjaja, A. Das, Ee-Luang Ang
{"title":"A fast neural network-based detection and tracking of dim moving targets in FLIR imagery","authors":"J. Patra, F. Widjaja, A. Das, Ee-Luang Ang","doi":"10.1109/IJCNN.2005.1556430","DOIUrl":"https://doi.org/10.1109/IJCNN.2005.1556430","url":null,"abstract":"Usually the targets in forward looking infra-red imagery are dim, slowly moving, and buried under clutter and noise. Detecting and tracking of such targets is a challenging task. Although artificial neural networks (ANNs) have been used to solve this problem, they need a lot of training time. In order to reduce the training time, we propose principal component analysis as a dimension reduction technique. We used an MLP with LM learning algorithm and a RBF neural network (RBFNN) with K-means algorithm to cluster the data. Both the ANNs are used in a neural adaptive line enhancer (NALE) configuration. Extensive computer simulations showed the combination of PCA and ANNs gives satisfactory results with significant reduction in training time.","PeriodicalId":365690,"journal":{"name":"Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.","volume":"435 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131222276","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: 9
Robustness of the NN Approach to emulating atmospheric long wave radiation in complex climate models 神经网络方法在复杂气候模式下模拟大气长波辐射的鲁棒性
Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005. Pub Date : 2005-12-27 DOI: 10.1109/IJCNN.2005.1556323
V. Krasnopolsky, M. Fox-Rabinovitz, M. Chou
{"title":"Robustness of the NN Approach to emulating atmospheric long wave radiation in complex climate models","authors":"V. Krasnopolsky, M. Fox-Rabinovitz, M. Chou","doi":"10.1109/IJCNN.2005.1556323","DOIUrl":"https://doi.org/10.1109/IJCNN.2005.1556323","url":null,"abstract":"In this paper we present comparisons of NN emulations developed for two different complex climate models: NCAR CAM model and NASA NSIPP. These models have different dynamics, different horizontal and vertical resolutions and different physics (including different long wave radiation schemes). Comparison of two (NCAR and NASA) NN emulations shows their profound similarity in terms of the accuracy of emulation vs. the original parameterizations and complexity of emulating NNs, i.e. the methodological robustness and portability of our NN emulation approach.","PeriodicalId":365690,"journal":{"name":"Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131248043","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
Streamflow forecasting using neural networks and fuzzy clustering techniques 利用神经网络和模糊聚类技术进行流量预测
Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005. Pub Date : 2005-12-27 DOI: 10.1109/IJCNN.2005.1556318
I. Luna, S. Soares, M. H. Magalhaes, R. Ballini
{"title":"Streamflow forecasting using neural networks and fuzzy clustering techniques","authors":"I. Luna, S. Soares, M. H. Magalhaes, R. Ballini","doi":"10.1109/IJCNN.2005.1556318","DOIUrl":"https://doi.org/10.1109/IJCNN.2005.1556318","url":null,"abstract":"Planning of hydroelectric systems is a complex and difficult task once it involves non-linear production characteristics and depends on numerous variables. A key variable is the streamflow. Streamflow values covering the entire planning period must be accurately forecasted because they strongly influence energy production. This paper suggests an application of a FIR neural network and a fuzzy clustering-based model to evaluate one-step and multi-step ahead predictions. Results are compared to the ones obtained by a periodic autoregressive model (PAR). It is interesting to apply a recurrent neural network for prediction task due to its ability for temporal processing and efficiency to solve nonlinear problems. The results show a generally better performance of the FIR neural network for the case studied.","PeriodicalId":365690,"journal":{"name":"Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132915309","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
Power system reduced model by artificial neural networks 电力系统人工神经网络约简模型
Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005. Pub Date : 2005-12-27 DOI: 10.1109/IJCNN.2005.1556314
J. Ramirez
{"title":"Power system reduced model by artificial neural networks","authors":"J. Ramirez","doi":"10.1109/IJCNN.2005.1556314","DOIUrl":"https://doi.org/10.1109/IJCNN.2005.1556314","url":null,"abstract":"This paper is aimed to the application of artificial neural networks (ANN) for constructing a power system reduced model, also termed dynamic equivalent. ANN are trained to help in constructing dynamic equivalents, which is considered a hard task in the context of electrical power systems. The main objective is to reproduce the complex voltage at some relevant nodes. The simulation results prove the applicability and robustness of this innovative approach.","PeriodicalId":365690,"journal":{"name":"Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133702375","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
Investigation of alternative strategies and quality measures for controlling the growth process of the growing hierarchical self-organizing map 层次自组织地图生长过程控制的备选策略和质量度量研究
Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005. Pub Date : 2005-12-27 DOI: 10.1109/IJCNN.2005.1556395
M. Dittenbach, A. Rauber, G. Polzlbauer
{"title":"Investigation of alternative strategies and quality measures for controlling the growth process of the growing hierarchical self-organizing map","authors":"M. Dittenbach, A. Rauber, G. Polzlbauer","doi":"10.1109/IJCNN.2005.1556395","DOIUrl":"https://doi.org/10.1109/IJCNN.2005.1556395","url":null,"abstract":"The self-organizing map (SOM) is a very popular neural network model for data analysis and visualization of high-dimensional input data. The growing hierarchical self-organizing map (GHSOM) - being one of the many architectures based on the SOM - has the property of dynamically adapting its architecture during training by map growth as well as creating a hierarchical structure of maps, thus reflecting hierarchical relations in the data. This allows for viewing portions of the data at different levels of granularity. We review different SOM quality measures and also investigate alternative strategies as candidates for guiding the growth process of the GHSOM in order to improve the hierarchical representation of the data.","PeriodicalId":365690,"journal":{"name":"Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.","volume":"227 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133702436","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: 10
Analyzing the state space property of echo state networks for chaotic system prediction 混沌系统预测回波状态网络的状态空间特性分析
Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005. Pub Date : 2005-12-27 DOI: 10.1109/IJCNN.2005.1556081
Jianhui Xi, Zhiwei Shi, Min Han
{"title":"Analyzing the state space property of echo state networks for chaotic system prediction","authors":"Jianhui Xi, Zhiwei Shi, Min Han","doi":"10.1109/IJCNN.2005.1556081","DOIUrl":"https://doi.org/10.1109/IJCNN.2005.1556081","url":null,"abstract":"For chaotic system prediction, ESNs (echo state networks) are realization of neural state reconstruction, in which the reconstructed state variable is from the internal neurons' activation, rather than the delay vector obtained from delay coordinate reconstruction. In the framework of the neural state reconstruction, some quantitative analyses can be further made on the issues such as the network structure configuration and initial state determination. Based on the simulation study on chaotic data from Chua's circuit, it is shown that the ESN is a non-minimum state space realization of the target time series, and the initial state can be freely chosen in the training process, and in the phase of prediction, ESN needs to know where the prediction begins by being set a proper initial state through a process of teacher forcing.","PeriodicalId":365690,"journal":{"name":"Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127651808","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
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