The 2012 International Joint Conference on Neural Networks (IJCNN)最新文献_第2页

The prediction approach with Growing Hierarchical Self-Organizing Map 基于增长层次自组织映射的预测方法

The 2012 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2012-06-10 DOI: 10.1109/IJCNN.2012.6252479

Shin-Ying Huang, R. Tsaih

{"title":"The prediction approach with Growing Hierarchical Self-Organizing Map","authors":"Shin-Ying Huang, R. Tsaih","doi":"10.1109/IJCNN.2012.6252479","DOIUrl":"https://doi.org/10.1109/IJCNN.2012.6252479","url":null,"abstract":"The competitive learning nature of the Growing Hierarchical Self-Organizing Map (GHSOM), which is an unsupervised neural networks extended from Self-Organizing Map (SOM), can work as a regularity detector that is supposed to help discover statistically salient features of the sample population. With the spatial correspondent assumption, this study presents a prediction approach in which GHSOM is used to help identify the fraud counterpart of each non-fraud subgroup and vice versa. In this study, two GHSOMs-a non-fraud tree (NFT) and a fraud tree (FT) are generated via the non-fraud samples and the fraud samples, respectively. Each (fraud or non-fraud) training sample is classified into its belonging leaf nodes of NFT and FT. Then, two classification rules are tuned based upon all training samples to determine the associated discrimination boundary within each leaf node, and the rule with better classification performance is chosen as the prediction rule. With the spatial correspondent assumption, the prediction rule derived from such an integration of FT and NFT classification mechanisms should work well. This study sets up the experiment of fraudulent financial reporting (FFR), a sub-field of financial fraud detection (FFD), to justify the effectiveness of the proposed prediction approach and the result is quite acceptable.","PeriodicalId":287844,"journal":{"name":"The 2012 International Joint Conference on Neural Networks (IJCNN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124885928","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: 11

Memristor models for chaotic neural circuits 混沌神经电路的忆阻器模型

The 2012 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2012-06-10 DOI: 10.1109/IJCNN.2012.6252777

F. Corinto, A. Ascoli, M. Gilli

引用次数: 14

Self organising map based region of interest labelling for automated defect identification in large sewer pipe image collections 基于自组织地图的兴趣区域标记用于大型污水管道图像集缺陷自动识别

The 2012 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2012-06-10 DOI: 10.1109/IJCNN.2012.6252482

Hiran Ganegedara, D. Alahakoon, J. Mashford, A. Paplinski, K. Müller, T. Deserno

{"title":"Self organising map based region of interest labelling for automated defect identification in large sewer pipe image collections","authors":"Hiran Ganegedara, D. Alahakoon, J. Mashford, A. Paplinski, K. Müller, T. Deserno","doi":"10.1109/IJCNN.2012.6252482","DOIUrl":"https://doi.org/10.1109/IJCNN.2012.6252482","url":null,"abstract":"Proper maintenance of sewer pipes is vital for the healthy functioning of a city. Due to the difficulty of reach for sewage pipes, automating pipe inspection has high potential in providing an efficient and objective identification of defects which could lead to damaging the pipe system. A popular approach has been to send remote controlled robots to photograph the pipes and process the images to identify possible defects. However majority of the images contain regular pipe features such as the flow line, pipe joints and pipe connections. Regular features pose a challenge for automated defect detection algorithms which require high processing time. This paper proposes a self organising map based approach to leverage the regularity of image features to isolate regions of interest which could contain defects. As a result, the search space is narrowed down for the defect detection algorithms, decreasing the overall processing time. Novelty of the work lies in the feature extraction and the gradual isolation of the potential defective image features to a manageable size. Therefore, this technique is suitable for large scale real applications. We demonstrate the effectiveness of the proposed approach for a real pipe image data set.","PeriodicalId":287844,"journal":{"name":"The 2012 International Joint Conference on Neural Networks (IJCNN)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115205891","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

Traffic modeling and identification using a Self-adaptive Fuzzy Inference Network 基于自适应模糊推理网络的交通建模与识别

The 2012 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2012-06-10 DOI: 10.1109/IJCNN.2012.6252621

S. W. Tung, Hiok Chai Quek, Cuntai Guan

引用次数: 1

Chebyshev Functional Link Neural Network-based modeling and experimental verification for photovoltaic arrays 基于Chebyshev功能链接神经网络的光伏阵列建模与实验验证

The 2012 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2012-06-10 DOI: 10.1109/IJCNN.2012.6252615

L. Jiang, D. Maskell, J. Patra

{"title":"Chebyshev Functional Link Neural Network-based modeling and experimental verification for photovoltaic arrays","authors":"L. Jiang, D. Maskell, J. Patra","doi":"10.1109/IJCNN.2012.6252615","DOIUrl":"https://doi.org/10.1109/IJCNN.2012.6252615","url":null,"abstract":"This paper presents a Chebyshev Functional Link Neural Network (CFLNN) based model for photovoltaic modules. There are two basic approaches to build a model - use an analytical modeling technique or use an Artificial Neural Network (ANN) based method. However, both the analytical modeling technique and the traditional Multilayer Perceptron (MLP) model have some disadvantages. For example, in the analytical model, the influence of irradiance and temperature on some parameters of the photovoltaic module, such as the parallel and series resistance and other uncertainty factors, are not taken into consideration. In the case of the multilayer neural network model, there is a large computational complexity in training the network and in its implementation. In order to overcome these advantages, we propose a CFLNN based model for solar modules. The proposed model not only reduces the complexity of the network due to the absence of hidden layers in the network configuration, but also shows better accuracy over the analytical modeling method. In the experimental section, the operating current predicted by CFLNN is compared with the outputs from other two modeling methods - MLP and the two-diode model. Finally, verification is performed using experimental datasets. The results show that the CFLNN modeling method provides better prediction of the output current compared to the analytical model and has a reduced computational complexity than the traditional MLP model.","PeriodicalId":287844,"journal":{"name":"The 2012 International Joint Conference on Neural Networks (IJCNN)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122959336","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: 19

A new Granular Computing approach for sequences representation and classification 一种新的序列表示和分类的颗粒计算方法

The 2012 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2012-06-10 DOI: 10.1109/IJCNN.2012.6252680

A. Rizzi, G. D. Vescovo, L. Livi, F. Mascioli

引用次数: 24

Laplacian Regularized Subspace Learning for interactive image re-ranking 基于拉普拉斯正则化子空间学习的交互式图像重排序

The 2012 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2012-06-10 DOI: 10.1109/IJCNN.2012.6252410

Lining Zhang, Lipo Wang, Weisi Lin

引用次数: 0

Feature selection based on sparse imputation 基于稀疏插值的特征选择

The 2012 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2012-06-10 DOI: 10.1109/IJCNN.2012.6252639

Jin Xu, Yafeng Yin, H. Man, Haibo He

引用次数: 33

Composite kernel based SVM for hierarchical multi-label gene function classification 基于复合核的支持向量机分层多标签基因功能分类

The 2012 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2012-06-10 DOI: 10.1109/IJCNN.2012.6252555

Benhui Chen, Lihua Duan, Jinglu Hu

引用次数: 5

Body area segmentation from visual scene based on predictability of neuro-dynamical system 基于神经动力系统可预测性的视觉场景体区域分割

The 2012 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2012-06-10 DOI: 10.1109/IJCNN.2012.6252530

H. Nobuta, Kenta Kawamoto, K. Noda, K. Sabe, S. Nishide, HIroshi G. Okuno, T. Ogata

{"title":"Body area segmentation from visual scene based on predictability of neuro-dynamical system","authors":"H. Nobuta, Kenta Kawamoto, K. Noda, K. Sabe, S. Nishide, HIroshi G. Okuno, T. Ogata","doi":"10.1109/IJCNN.2012.6252530","DOIUrl":"https://doi.org/10.1109/IJCNN.2012.6252530","url":null,"abstract":"We propose neural models for segmenting the area of a body from visual scene based on predictability. Neuroscience has shown that a prediction model in brain, which predicts sensory-feedback from motor command, can divide the sensory-feedback into the self-motion derived feedback and other derived feedback. The prediction model is important for prediction control of the body. Previous studies in robotics of the prediction model assumed that a robot can recognize the position of its body (e.g. its hand) and that the view contains only that body part. In our models, motor commands and visual feedback (pixel image that includes not only a hand but also object and background) are input into a neural network model and then the body area is segmented and prediction model of body is acquired. Our model contains two parts: 1) An object detection model obtains a conversion system between object positions and the pixel image. 2) A movement prediction model predicts hand-object positions from motor commands and identifies the body. We confirmed that our models can segment the body/object area based on their pixel textures and discriminate between them by using prediction error.","PeriodicalId":287844,"journal":{"name":"The 2012 International Joint Conference on Neural Networks (IJCNN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128571366","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