2016 International Joint Conference on Neural Networks (IJCNN)最新文献_第9页

Automatic detection of bike-riders without helmet using surveillance videos in real-time 利用监控视频实时自动检测无头盔骑自行车者

2016 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2016-07-24 DOI: 10.1109/IJCNN.2016.7727586

Kunal Dahiya, Dinesh Singh, C. Mohan

引用次数: 97

Deep self-organizing reservoir computing model for visual object recognition 面向视觉目标识别的深度自组织库计算模型

2016 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2016-07-24 DOI: 10.1109/IJCNN.2016.7727351

Zhidong Deng, Chengzhi Mao, Xiong Chen

{"title":"Deep self-organizing reservoir computing model for visual object recognition","authors":"Zhidong Deng, Chengzhi Mao, Xiong Chen","doi":"10.1109/IJCNN.2016.7727351","DOIUrl":"https://doi.org/10.1109/IJCNN.2016.7727351","url":null,"abstract":"Reservoir computing becomes increasingly a hot spot in recent years. In this paper, we propose a deep self-organizing reservoir computing model for visual object recognition. First, through combination of Kohonen's self-organizing map and SHESN network, we present a self-organizing SHESN (SO-SHESN). In the new model, we adopt the same mechanism of generating reservoir as SHESN, but McCulloch-Pitts type reservoir neuron is replaced with radial basis function neuron. Correspondingly, unsupervised competitive learning is exploited to train both input weights and reservoir weights of SO-SHESN. Second, we propose a deep SO-SHESN model through a stack of well-trained reservoir layers. In such a stacked structure, a novel trial-and-readout learning algorithm is used for pre-training of layer-wise reservoir, in which each layer is trained independently from each other. Finally, the experimental results obtained on MNIST benchmark dataset show that our SO-SHESN achieves the test recognition error rate of 5.66%, which improves classical ESN and SHESN by 6.44% and 1.74%, respectively. Furthermore, the test error rate of our deep SO-SHESN could reach up to 1.39%, which outperforms SO-SHESN with single reservoir layer by 4.27% and approximately approaches the state-of-the-art result of 1% among existing traditional machine learning approaches with non-CNN features.","PeriodicalId":109405,"journal":{"name":"2016 International Joint Conference on Neural Networks (IJCNN)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126783829","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

Accommodative neural filters 调节神经滤波器

2016 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2016-07-24 DOI: 10.1109/IJCNN.2016.7727490

J. Lo, Yu Guo

引用次数: 3

Forecasting wind power - an ensemble technique with gradual coopetitive weighting based on weather situation 预测风力发电——一种基于天气情况的渐进协同加权集成技术

2016 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2016-07-24 DOI: 10.1109/IJCNN.2016.7727855

André Gensler, B. Sick

{"title":"Forecasting wind power - an ensemble technique with gradual coopetitive weighting based on weather situation","authors":"André Gensler, B. Sick","doi":"10.1109/IJCNN.2016.7727855","DOIUrl":"https://doi.org/10.1109/IJCNN.2016.7727855","url":null,"abstract":"The prediction of the power generation of wind farms is a non-trivial problem with increasing importance during the last decade due to the rapid increase of wind power generation in the power grid. The prediction task is commonly addressed using numerical weather predictions, statistical methods, or machine learning techniques. Various articles have shown that ensemble techniques for forecasting can yield better results regarding forecasting accuracy than single techniques alone. Typical ensembles make use of a parameter, or data diversity approach to build the models. In this article, we propose a novel ensemble technique using both, cooperative and competitive characteristics of ensembles to gradually adjust the influences of single forecasting algorithms in the ensemble based on their individual strengths using a “coopetitive” weighting formula. The observed quality of the models during training is used to adaptively weigh the models based on the location in the input data space (i.e., depending on the weather situation). We compute the overall weights for a particular weather situation using both, a spatial as well as a global weighting term. The experimental evaluation is performed on a data set consisting of data from 45 wind farms, which is made publicly available. We demonstrate that the technique is among the best performing algorithms compared to other state-of-the-art algorithms and ensembles. Furthermore, the practical applicability of the proposed technique is discussed.","PeriodicalId":109405,"journal":{"name":"2016 International Joint Conference on Neural Networks (IJCNN)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126911836","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: 20

Optimal output synchronization of nonlinear multi-agent systems using approximate dynamic programming 基于近似动态规划的非线性多智能体系统最优输出同步

2016 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2016-07-24 DOI: 10.1109/IJCNN.2016.7727751

H. Modares, F. Lewis, A. Davoudi

引用次数: 6

A firefly algorithm for modular granular neural networks optimization applied to iris recognition 模块化颗粒神经网络优化萤火虫算法在虹膜识别中的应用

2016 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2016-07-24 DOI: 10.1109/IJCNN.2016.7727191

D. Sánchez, P. Melin, Juan Martín Carpio Valadez, Héctor José Puga Soberanes

引用次数: 8

kNN ensembles with penalized DTW for multivariate time series imputation 多变量时间序列imputation中带有惩罚DTW的kNN集成

2016 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2016-07-24 DOI: 10.1109/IJCNN.2016.7727549

Stefan Oehmcke, O. Zielinski, Oliver Kramer

引用次数: 37

Multilayer perceptron with Cuckoo search in water level prediction for flood forecasting 基于布谷鸟搜索的多层感知器在洪水水位预测中的应用

2016 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2016-07-24 DOI: 10.1109/IJCNN.2016.7727243

Suwannee Phitakwinai, S. Auephanwiriyakul, N. Theera-Umpon

引用次数: 10

ELM based multiple kernel k-means with diversity-induced regularization 基于ELM的多核k-均值分集正则化算法

2016 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2016-07-24 DOI: 10.1109/IJCNN.2016.7727538

Yang Zhao, Y. Dou, Xinwang Liu, Teng Li

引用次数: 1

Efficient computation of the Levenberg-Marquardt algorithm for feedforward networks with linear outputs 线性输出前馈网络Levenberg-Marquardt算法的高效计算

2016 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2016-07-24 DOI: 10.1109/IJCNN.2016.7727182

P. Chazal, M. McDonnell

引用次数: 1