2007 International Joint Conference on Neural Networks最新文献_第7页

A Recurrent Neural Network for Non-smooth Nonlinear Programming Problems 非光滑非线性规划问题的递归神经网络

2007 International Joint Conference on Neural Networks Pub Date : 2007-10-29 DOI: 10.1109/IJCNN.2007.4371024

L. Cheng, Z. Hou, M. Tan, Xiuqing Wang, Zeng-Shun Zhao, Sanqing Hu

引用次数: 9

Feature Extraction of Waveform Signals for Uncertain Dynamic Processes Using Neural Networks 基于神经网络的不确定动态过程波形信号特征提取

2007 International Joint Conference on Neural Networks Pub Date : 2007-10-29 DOI: 10.1109/IJCNN.2007.4371338

Yaw-Jen Chang, C. Chang, Jui-Ju Tsai

引用次数: 1

The Neural Network Approach to Automatic Construction of Adaptive Meshes on Multiply-connected Domains 多连通域自适应网格自动构建的神经网络方法

2007 International Joint Conference on Neural Networks Pub Date : 2007-10-29 DOI: 10.1109/IJCNN.2007.4371250

O. Nechaeva

引用次数: 1

Theories of Neural Networks Leading to Unsupervised Learning 导致无监督学习的神经网络理论

2007 International Joint Conference on Neural Networks Pub Date : 2007-10-29 DOI: 10.1109/IJCNN.2007.4371458

H. Szu

引用次数: 1

Real Time Credit Card Fraud Detection using Computational Intelligence 基于计算智能的实时信用卡欺诈检测

2007 International Joint Conference on Neural Networks Pub Date : 2007-10-29 DOI: 10.1109/IJCNN.2007.4371071

Jon T. S. Quah, M. Sriganesh

引用次数: 174

Optimal Control of a Photovoltaic Solar Energy System with Adaptive Critics 具有自适应批评的光伏太阳能系统的最优控制

2007 International Joint Conference on Neural Networks Pub Date : 2007-10-29 DOI: 10.1109/IJCNN.2007.4371092

R. L. Welch, G. Venayagamoorthy

{"title":"Optimal Control of a Photovoltaic Solar Energy System with Adaptive Critics","authors":"R. L. Welch, G. Venayagamoorthy","doi":"10.1109/IJCNN.2007.4371092","DOIUrl":"https://doi.org/10.1109/IJCNN.2007.4371092","url":null,"abstract":"This paper presents an optimal energy control scheme for a grid independent photovoltaic (PV) solar system consisting of a PV array, battery energy storage, and time varying loads (a small critical load and a larger variable non-critical load). The optimal controller design is based on a class of adaptive critic designs (ACDs) called the action dependant heuristic dynamic programming (ADHDP). The ADHDP class of ACDs uses two neural networks, an \"action\" network (which actually dispenses the control signals) and a \"critic\" network (which critics the action network performance). An optimal control policy is evolved by the action network over a period of time using the feedback signals provided by the critic network. The objectives of the optimal controller in order of decreasing importance is to first fully dispatch the required energy to the critical loads at all times; secondly to dispatch energy to the battery whenever necessary so as to be able to dispatch energy to the critical loads in any absence of energy from the PV array; and lastly to dispatch energy to the non-critical loads while not interfering with the first two objectives. Results on three different US cities show that the ADHDP based optimal control scheme outperforms the conventional PV-priority control scheme in maintaining the stated objectives almost all the time.","PeriodicalId":350091,"journal":{"name":"2007 International Joint Conference on Neural Networks","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116404213","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: 16

Prediction of Long-range Contacts from Sequence Profile 从序列剖面预测远程接触

2007 International Joint Conference on Neural Networks Pub Date : 2007-10-29 DOI: 10.1109/IJCNN.2007.4371084

Peng Chen, Bing Wang, H. Wong, De-shuang Huang

引用次数: 5

FPGA Implementation of Pulse Density Hopfield Neural Network 脉冲密度Hopfield神经网络的FPGA实现

2007 International Joint Conference on Neural Networks Pub Date : 2007-10-29 DOI: 10.1109/IJCNN.2007.4371042

Y. Maeda, Yoshinori Fukuda

引用次数: 10

Maximum Margin based Semi-supervised Spectral Kernel Learning 基于最大边际的半监督谱核学习

2007 International Joint Conference on Neural Networks Pub Date : 2007-10-29 DOI: 10.1109/IJCNN.2007.4370993

Zenglin Xu, Jianke Zhu, Michael R. Lyu, Irwin King

{"title":"Maximum Margin based Semi-supervised Spectral Kernel Learning","authors":"Zenglin Xu, Jianke Zhu, Michael R. Lyu, Irwin King","doi":"10.1109/IJCNN.2007.4370993","DOIUrl":"https://doi.org/10.1109/IJCNN.2007.4370993","url":null,"abstract":"Semi-supervised kernel learning is attracting increasing research interests recently. It works by learning an embedding of data from the input space to a Hilbert space using both labeled data and unlabeled data, and then searching for relations among the embedded data points. One of the most well-known semi-supervised kernel learning approaches is the spectral kernel learning methodology which usually tunes the spectral empirically or through optimizing some generalized performance measures. However, the kernel designing process does not involve the bias of a kernel-based learning algorithm, the deduced kernel matrix cannot necessarily facilitate a specific learning algorithm. To supplement the spectral kernel learning methods, this paper proposes a novel approach, which not only learns a kernel matrix by maximizing another generalized performance measure, the margin between two classes of data, but also leads directly to a convex optimization method for learning the margin parameters in support vector machines. Moreover, experimental results demonstrate that our proposed spectral kernel learning method achieves promising results against other spectral kernel learning methods.","PeriodicalId":350091,"journal":{"name":"2007 International Joint Conference on Neural Networks","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122050369","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: 5

Compact hardware for real-time speech recognition using a Liquid State Machine 紧凑的硬件实时语音识别使用的液体状态机

2007 International Joint Conference on Neural Networks Pub Date : 2007-10-29 DOI: 10.1109/IJCNN.2007.4371111

B. Schrauwen, Michiel D'Haene, D. Verstraeten, J. V. Campenhout

引用次数: 23