The 2012 International Joint Conference on Neural Networks (IJCNN)最新文献

Fuzzy associative learning of feature dependency for time series forecasting 特征依赖的模糊关联学习用于时间序列预测

The 2012 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2012-07-30 DOI: 10.1109/IJCNN.2012.6252542

E. Cheu, Kelvin Sim, See-Kiong Ng, Hiok Chai Quek

{"title":"Fuzzy associative learning of feature dependency for time series forecasting","authors":"E. Cheu, Kelvin Sim, See-Kiong Ng, Hiok Chai Quek","doi":"10.1109/IJCNN.2012.6252542","DOIUrl":"https://doi.org/10.1109/IJCNN.2012.6252542","url":null,"abstract":"Neuro-fuzzy system (NFS) has successfully been widely applied in solving problems across diverse fields, such as signal detection, fault detection, and forecasting. In recent years, many forecasting problems require the processing and learning of large number of dynamic data streams. Existing systems are inadequate in handling this type of complex problem. This paper presents a learning system that incorporates an evolving correlation-based feature selector to handle the high dimensionality of the data streams, and an evolving NFS to sequentially model and extract fuzzy knowledge about these data streams. The proposed system requires no prior knowledge of the data, reads the stream of data in a single pass, and accounts for the time-varying characteristics of the data. These three features allow the system to handle large and dynamic data. The effectiveness of the proposed system is validated on both synthetic and real-world problems. The experiments illustrate the viability of the proposed learning technique, and exemplifies how it can outperform existing NFS. Experiment on real-world stock price forecasting shows a remarkable reduction of error rate by 15.4%.","PeriodicalId":287844,"journal":{"name":"The 2012 International Joint Conference on Neural Networks (IJCNN)","volume":"321 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132908567","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

Nonlinear system identification based on SVR with quasi-linear kernel 基于拟线性核SVR的非线性系统辨识

The 2012 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2012-07-30 DOI: 10.1109/IJCNN.2012.6252694

Y. Cheng, Jinglu Hu

引用次数: 8

Improved link-based cluster ensembles 改进的基于链路的集群集成

The 2012 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2012-07-30 DOI: 10.1109/IJCNN.2012.6252757

Natthakan Iam-on, Tossapon Boongoen

引用次数: 9

An information theoretic kernel algorithm for robust online learning 鲁棒在线学习的信息核算法

The 2012 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2012-07-30 DOI: 10.1109/IJCNN.2012.6252837

Haijin Fan, Q. Song, Zhao Xu

引用次数: 2

Contextual multi-armed bandits for web server defense 用于web服务器防御的上下文多武装强盗

The 2012 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2012-07-30 DOI: 10.1109/IJCNN.2012.6252760

T. Jung, Sylvain Martin, D. Ernst, G. Leduc

引用次数: 2

Energy savings in HVAC systems using discrete model-based predictive control 基于离散模型的预测控制在暖通空调系统中的节能作用

The 2012 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2012-06-10 DOI: 10.1109/IJCNN.2012.6252538

P. Ferreira, Sergio Silva, A. Ruano

{"title":"Energy savings in HVAC systems using discrete model-based predictive control","authors":"P. Ferreira, Sergio Silva, A. Ruano","doi":"10.1109/IJCNN.2012.6252538","DOIUrl":"https://doi.org/10.1109/IJCNN.2012.6252538","url":null,"abstract":"The paper addresses the problem of controlling an heating ventilating and air conditioning system with the purpose of achieving a desired thermal comfort level and energy savings. The formulation uses the thermal comfort as a restriction and minimises the energy spent to comply with it. This results in the maintenance of thermal comfort and on the minimisation of energy, which in most operating conditions are conflicting goals requiring some sort of optimisation method to find appropriate solutions over time. In this work a discrete model based predictive control methodology is applied to the problem. It consists of three major components: the predictive models, implemented by radial basis function neural networks identified by means of a multi-objective genetic algorithm; the cost function that will be optimised to minimise energy consumption and provide adequate thermal comfort; and finally the optimisation method, in this case a discrete branch and bound approach. Each component will be described, and experimental results obtained within a classroom will be presented, demonstrating the feasibility and performance of the approach. Finally the energy savings resulting from the application of the method are estimated.","PeriodicalId":287844,"journal":{"name":"The 2012 International Joint Conference on Neural Networks (IJCNN)","volume":"51 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":"114665109","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

Incremental update of biometric models in face-based video surveillance 人脸视频监控中生物识别模型的增量更新

The 2012 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2012-06-10 DOI: 10.1109/IJCNN.2012.6252658

Miguel De-la-Torre, Eric Granger, P. Radtke, R. Sabourin, D. Gorodnichy

{"title":"Incremental update of biometric models in face-based video surveillance","authors":"Miguel De-la-Torre, Eric Granger, P. Radtke, R. Sabourin, D. Gorodnichy","doi":"10.1109/IJCNN.2012.6252658","DOIUrl":"https://doi.org/10.1109/IJCNN.2012.6252658","url":null,"abstract":"Video-based face recognition of individuals involves matching facial regions captured in video sequences against the model of individuals enrolled to a face recognition system. Due to a limited control over operational conditions, classification systems applied to face matching are confronted with complex pattern recognition environments that change over time. Therefore, the facial model of an individual tends to diverge from the underlying data distribution. Although a limited amount of reference data is often collected during initial enrollment, new samples often become available over time to update and refine models. In this paper, an adaptive ensemble of classifiers is proposed to update facial models in response to new reference samples. To avoid knowledge corruption linked to incremental learning of monolithic classifiers, and maintain a high level of performance, this ensemble exploits a learn-and-combine approach. In response to new reference samples, a new 2-class Probabilistic Fuzzy ARTMAP classifier is trained and combined to previously-trained classifiers in the ROC space. Iterative Boolean Combination is employed for fusion of 2-class classifiers of each individual in the decision space. Performance is assessed in terms of AUC accuracy and resource requirements under different incremental learning scenarios with new data extracted from the Faces in Action data set. Simulation results indicate that the proposed system significantly outperforms reference classifiers and ensembles for incremental learning.","PeriodicalId":287844,"journal":{"name":"The 2012 International Joint Conference on Neural Networks (IJCNN)","volume":"5 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":"116636784","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: 12

Adaptive dynamic programming with stable value iteration algorithm for discrete-time nonlinear systems 离散非线性系统的稳定值迭代自适应动态规划算法

The 2012 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2012-06-10 DOI: 10.1109/IJCNN.2012.6252512

Qinglai Wei, Derong Liu

引用次数: 12

Population-based routing in the SpiNNaker neuromorphic architecture SpiNNaker神经形态结构中基于种群的路由

The 2012 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2012-06-10 DOI: 10.1109/IJCNN.2012.6252635

Sergio Davies, J. Navaridas, F. Galluppi, S. Furber

引用次数: 23

Application of the IPSONet in face detection IPSONet在人脸检测中的应用

The 2012 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2012-06-10 DOI: 10.1109/IJCNN.2012.6252676

Elliackin M. N. Figueiredo, Rafael G. Mesquita, Teresa B Ludermir, George D. C. Cavalcanti

{"title":"Application of the IPSONet in face detection","authors":"Elliackin M. N. Figueiredo, Rafael G. Mesquita, Teresa B Ludermir, George D. C. Cavalcanti","doi":"10.1109/IJCNN.2012.6252676","DOIUrl":"https://doi.org/10.1109/IJCNN.2012.6252676","url":null,"abstract":"Artificial Neural Networks (ANNs) has been applied in the face detection task because of its ability to capture the complex probability distribution conditioned to the class of face patterns. However, many works use Back-Propagation (BP) to adapt the weights of the ANNs. The problem of using BP is that it has many disadvantages related to the appropriate choice of its parameters, as the learning rate and momentum. Furthermore, since BP assumes a fixed architecture for the ANN, an inappropriate choice of the architecture can make it have a sub-optimal performance. In this paper we investigate the application of the IPSONet in the facial detection task. IPSONet is a training technique for neural networks like multilayer perceptron (MLP) that uses an improved PSO to evolve simultaneously structure and weights of ANNs. Thus, the IPSONet produces ANNs with higher generalization ability if compared to BP. The system developed in this work, which includes the feature extraction process of the input image and the training of a MLP net using IPSONet is called IPSONetFD. The experiments using the MIT CBCL Face Database showed that the proposed technique is robust in the sense that it can detect faces with a wide variety of pose, lighting and face expression. The results showed that the IPSONetFD had better performance than others ANN's architectures (PyraNet and I-PyraNet, in this study), and an equivalent performance if compared to SVM. Thus, the proposed technique demonstrated that ANNs trained by IPSONet has better performance than ANNs trained by BP in the face detection task.","PeriodicalId":287844,"journal":{"name":"The 2012 International Joint Conference on Neural Networks (IJCNN)","volume":"30 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":"127169051","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: 2