The 2011 International Joint Conference on Neural Networks最新文献_第2页

The effects of feedback and lateral connections on perceptual processing: A study using oscillatory networks 反馈和横向连接对知觉加工的影响:一项使用振荡网络的研究

The 2011 International Joint Conference on Neural Networks Pub Date : 2011-10-03 DOI: 10.1109/IJCNN.2011.6033357

A. R. Rao, G. Cecchi

引用次数: 7

RANSAC algorithm with sequential probability ratio test for robust training of feed-forward neural networks 基于序列概率比检验的RANSAC算法用于前馈神经网络的鲁棒训练

The 2011 International Joint Conference on Neural Networks Pub Date : 2011-10-03 DOI: 10.1109/IJCNN.2011.6033653

M. El-Melegy

{"title":"RANSAC algorithm with sequential probability ratio test for robust training of feed-forward neural networks","authors":"M. El-Melegy","doi":"10.1109/IJCNN.2011.6033653","DOIUrl":"https://doi.org/10.1109/IJCNN.2011.6033653","url":null,"abstract":"This paper addresses the problem of fitting a functional model to data corrupted with outliers using a multilayered feed-forward neural network (MFNN). Almost all previous efforts to solve this problem have focused on using a training algorithm that minimizes an M-estimator based error criterion. However the robustness gained from M-estimators is still low. Using a training algorithm based on the RANdom SAmple Consensus (RANSAC) framework improves significantly the robustness of the algorithm. However the algorithm typically requires prolonged period of time before a final solution is reached. In this paper, we propose a new strategy to improve the time performance of the RANSAC algorithm for training MFNNs. A statistical pre-test based on Wald's sequential probability ratio test (SPRT) is performed on each randomly generated sample to decide whether it deserves to be used for model estimation. The proposed algorithm is evaluated on synthetic data, contaminated with varying degrees of outliers, and have demonstrated faster performance compared to the original RANSAC algorithm with no significant sacrifice of the robustness.","PeriodicalId":415833,"journal":{"name":"The 2011 International Joint Conference on Neural Networks","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125009023","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: 14

Ant Colony Optimization Changing the Rate of Dull Ants and its application to QAP 蚁群优化改变迟钝蚁率及其在QAP中的应用

The 2011 International Joint Conference on Neural Networks Pub Date : 2011-10-03 DOI: 10.1109/IJCNN.2011.6033592

Shozo Shimomura, H. Matsushita, Y. Nishio

引用次数: 0

Variations to incremental growing neural gas algorithm based on label maximization 基于标签最大化的增量增长神经气体算法的改进

The 2011 International Joint Conference on Neural Networks Pub Date : 2011-10-03 DOI: 10.1109/IJCNN.2011.6033326

Jean-Charles Lamirel, Raghvendra Mall, Pascal Cuxac, Ghada Safi

引用次数: 38

Development of a mix-design based Rapid Chloride Permeability assessment model using neuronets 基于混合设计的神经网络氯化物渗透率快速评估模型的开发

The 2011 International Joint Conference on Neural Networks Pub Date : 2011-10-03 DOI: 10.1109/IJCNN.2011.6033580

Hakan Yasarer, Y. Najjar

{"title":"Development of a mix-design based Rapid Chloride Permeability assessment model using neuronets","authors":"Hakan Yasarer, Y. Najjar","doi":"10.1109/IJCNN.2011.6033580","DOIUrl":"https://doi.org/10.1109/IJCNN.2011.6033580","url":null,"abstract":"Corrosion of reinforcing steel due to chloride penetration is one of the most common causes of deterioration in concrete pavement structures. On an annual basis, millions of dollars are spent on corrosion-related repairs. High incidence rates and repair costs have stimulated widespread research interests in order to properly assess the durability problem of concrete pavements. Chloride penetration of concrete pavement structures is determined through the Rapid Chloride Permeability test (RCPT), which typically measures the number of coulombs passing through a concrete sample over a period of six hours at a concrete age of 7, 28, and 56 days. In a composite material, such as concrete, the parameters of the mixture design and interaction between them determine the behavior of the material. Previous studies have shown that Artificial Neural Network (ANN) based material modeling approach has been successfully used to capture complex interactions among input and output variables. In this study, back-propagation ANN, and Regression-based permeability response prediction models were developed to assess the permeability potential of various concrete mixes using data obtained from actual Rapid Chloride Permeability tests. The back-propagation ANN learning technique proved to be an efficient method to produce relatively accurate permeability response prediction models. Comparison of the prediction accuracy of the developed ANN models and the regression model proved that the developed ANN model outperformed the regression-based model. The developed ANN models have high predictive capability to properly assess the chloride permeability of concrete mixes based on various mix-design parameters. These models can reliably be used for permeability prediction tasks in order to reduce or eliminate the duration of the testing as well as the sample preparation periods required for proper RCP testing.","PeriodicalId":415833,"journal":{"name":"The 2011 International Joint Conference on Neural Networks","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126068594","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

Group lasso regularized multiple kernel learning for heterogeneous feature selection 基于组lasso正则化多核学习的异构特征选择

The 2011 International Joint Conference on Neural Networks Pub Date : 2011-10-03 DOI: 10.1109/IJCNN.2011.6033554

Yi-Ren Yeh, Y. Chung, Ting-Chu Lin, Y. Wang

{"title":"Group lasso regularized multiple kernel learning for heterogeneous feature selection","authors":"Yi-Ren Yeh, Y. Chung, Ting-Chu Lin, Y. Wang","doi":"10.1109/IJCNN.2011.6033554","DOIUrl":"https://doi.org/10.1109/IJCNN.2011.6033554","url":null,"abstract":"We propose a novel multiple kernel learning (MKL) algorithm with a group lasso regularizer, called group lasso regularized MKL (GL-MKL), for heterogeneous feature selection. We extend the existing MKL algorithm and impose a mixed ℓ1 and ℓ2 norm constraint (known as group lasso) as the regularizer. Our GL-MKL determines the optimal base kernels, including the associated weights and kernel parameters, and results in a compact set of features for comparable or improved recognition performance. The use of our GL-MKL avoids the problem of choosing the proper technique to normalize the feature attributes collected from heterogeneous domains (and thus with different properties and distribution ranges). Our approach does not need to exhaustively search for the entire feature space when performing feature selection like prior sequential-based feature selection methods did, and we do not require any prior knowledge on the optimal size of the feature subset either. Comparisons with existing MKL or sequential-based feature selection methods on a variety of datasets confirm the effectiveness of our method in selecting a compact feature subset for comparable or improved classification performance.","PeriodicalId":415833,"journal":{"name":"The 2011 International Joint Conference on Neural Networks","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123261262","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

Advances on criteria for biological plausibility in artificial neural networks: Think of learning processes 人工神经网络生物合理性标准的研究进展:以学习过程为例

The 2011 International Joint Conference on Neural Networks Pub Date : 2011-10-03 DOI: 10.1109/IJCNN.2011.6033387

Alberione Braz da Silva, J. Rosa

{"title":"Advances on criteria for biological plausibility in artificial neural networks: Think of learning processes","authors":"Alberione Braz da Silva, J. Rosa","doi":"10.1109/IJCNN.2011.6033387","DOIUrl":"https://doi.org/10.1109/IJCNN.2011.6033387","url":null,"abstract":"Artificial neural network (ANN) community is engaged in biological plausibility issues these days. Different views about this subject can lead to disagreements of classification criteria among ANN researchers. In order to contribute to this debate, two of these views are highlighted here: one is related directly to the cerebral cortex biological structure, and the other focuses the neural features and the signaling between neurons. The model proposed in this paper considers that a biologically more plausible ANN has the purpose to create a more faithful model concerning the biological structure, properties, and functionalities, including learning processes, of the cerebral cortex, not disregarding its computational efficiency. The choice of the models upon which the proposed description is based takes into account two main criteria: the fact they are considered biologically more realistic and the fact they deal with intra and inter-neuron signaling in electrical and chemical synapses. Also, the duration of action potentials is taken into account. In addition to the characteristics for encoding information regarding biological plausibility present in current spiking neuron models, a distinguishable feature is emphasized here: a combination of Hebbian learning and error-driven learning.","PeriodicalId":415833,"journal":{"name":"The 2011 International Joint Conference on Neural Networks","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123543839","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

Selective adjustment of rotationally-asymmetric neuron σ-widths 旋转不对称神经元σ-宽度的选择性调节

The 2011 International Joint Conference on Neural Networks Pub Date : 2011-10-03 DOI: 10.1109/IJCNN.2011.6033392

Nathan Rose

引用次数: 0

Prediction of electric power consumption for commercial buildings 商业建筑用电量预测

The 2011 International Joint Conference on Neural Networks Pub Date : 2011-10-03 DOI: 10.1109/IJCNN.2011.6033285

V. Cherkassky, S. Chowdhury, Volker Landenberger, Saurabh Tewari, P. Bursch

引用次数: 20

Traffic sign recognition with multi-scale Convolutional Networks 基于多尺度卷积网络的交通标志识别

The 2011 International Joint Conference on Neural Networks Pub Date : 2011-10-03 DOI: 10.1109/IJCNN.2011.6033589

P. Sermanet, Yann LeCun

引用次数: 729