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2007 International Joint Conference on Neural Networks最新文献

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Dynamic Pooling for the Combination of Forecasts generated using Multi Level Learning 多层学习生成的预测组合的动态池化
2007 International Joint Conference on Neural Networks Pub Date : 2007-10-29 DOI: 10.1109/IJCNN.2007.4370999
Silvia Riedel, B. Gabrys
{"title":"Dynamic Pooling for the Combination of Forecasts generated using Multi Level Learning","authors":"Silvia Riedel, B. Gabrys","doi":"10.1109/IJCNN.2007.4370999","DOIUrl":"https://doi.org/10.1109/IJCNN.2007.4370999","url":null,"abstract":"In this paper we provide experimental results and extensions to our previous theoretical findings concerning the combination of forecasts that have been diversified by three different methods: with parameters learned at different data aggregation levels, by thick modeling and by the use of different forecasting methods. An approach of error variance based pooling as proposed by Aiolfi and Timmermann has been compared with flat combinations as well as an alternative pooling approach in which we consider information about the used diversification. An advantage of our approach is that it leads to the generation of novel multi step multi level forecast generation structures that carry out the combination in different steps of pooling corresponding to the different types of diversification. We describe different evolutionary approaches in order to evolve the order of pooling of the diversification dimensions. Extensions of such evolutions allow the generation of more flexible multi level multi step combination structures containing better adaptive capabilities. We could prove a significant error reduction comparing results of our generated combination structures with results generated with the algorithm of Aiolfi and Timmermann as well as with flat combination for the application of Revenue Management seasonal forecasting.","PeriodicalId":350091,"journal":{"name":"2007 International Joint Conference on Neural Networks","volume":"49 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":"127514578","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
Fault Diagnosis of an Actuator in the Attitude Control Subsystem of a Satellite using Neural Networks 基于神经网络的卫星姿态控制子系统作动器故障诊断
2007 International Joint Conference on Neural Networks Pub Date : 2007-10-29 DOI: 10.1109/IJCNN.2007.4371378
Zhongqi Li, Liying Ma, K. Khorasani
{"title":"Fault Diagnosis of an Actuator in the Attitude Control Subsystem of a Satellite using Neural Networks","authors":"Zhongqi Li, Liying Ma, K. Khorasani","doi":"10.1109/IJCNN.2007.4371378","DOIUrl":"https://doi.org/10.1109/IJCNN.2007.4371378","url":null,"abstract":"The goal of this paper is to develop a neural network-based scheme for fault detection and isolation in reaction wheels (actuators) of a satellite. To achieve this objective, three neural networks are developed for modeling the dynamics of a reaction wheel on all the three axes separately. A recurrent neural network with backpropagation training algorithm is considered for representing the highly nonlinear dynamics of the actuator. The capabilities and potential of the proposed neural network-based fault detection and isolation (FDI) methodology is investigated and a comparative study is conducted with the performance of a generalized Luenberger observer-based scheme. Simulation results demonstrate clearly the advantages of our proposed neural network scheme studied in this paper.","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":"127515714","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
Variational Bayes Inference for Generalized Associative Functional Networks 广义联想泛函网络的变分贝叶斯推理
2007 International Joint Conference on Neural Networks Pub Date : 2007-10-29 DOI: 10.1109/IJCNN.2007.4370952
Hanbing Qu, Bao-Gang Hu
{"title":"Variational Bayes Inference for Generalized Associative Functional Networks","authors":"Hanbing Qu, Bao-Gang Hu","doi":"10.1109/IJCNN.2007.4370952","DOIUrl":"https://doi.org/10.1109/IJCNN.2007.4370952","url":null,"abstract":"We propose a Bayesian framework for generalized associative functional networks (GAFN) and provide variational Bayes (VB) learning algorithm to approximate the posterior distributions over parameters of GAFN. The learning procedure for GAFN involves equality constraints on parameters, thus conventional approaches, like probabilistic graphical model or Lagrange multiplier method, will be inconvenient or expensive for solving the GAFN model in a direct way. We provide a linear transformation algorithm for the learning of parameters of GAFN. By means of the linear transformation, the evaluation of Lagrange multipliers is avoided and an iterative VB approximate procedure is restricted to a subspace of the original weight space. The VB framework naturally prevents overfltting and statistical inference can be made conveniently for weights of GAFN by the approximate posterior distributions over weights. The Bayesian GAFN is applied to autoregressive time series and the experimental results are comparable to other existing methods.","PeriodicalId":350091,"journal":{"name":"2007 International Joint Conference on Neural Networks","volume":"48 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":"130018818","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
A New Semantic Network Program Based on Combination of Case Knowledge and General Knowledge 一种基于Case知识和General Knowledge相结合的语义网络程序
2007 International Joint Conference on Neural Networks Pub Date : 2007-10-29 DOI: 10.1109/IJCNN.2007.4371014
Kazuo Nishimura
{"title":"A New Semantic Network Program Based on Combination of Case Knowledge and General Knowledge","authors":"Kazuo Nishimura","doi":"10.1109/IJCNN.2007.4371014","DOIUrl":"https://doi.org/10.1109/IJCNN.2007.4371014","url":null,"abstract":"This paper proposes a new semantic network program that combines case knowledge with well-defined general knowledge. The proposed semantic network program has been applied to scene understanding problem. When the network receives a scene image, it outputs candidates for an ambiguous image object in the scene image based on activation spreading on the network. Java programming language has been employed to program the semantic network. It has been shown that object-oriented principle fits semantic networks and makes them a practical tool to realize human-like information processing. Simulation of image object identification has been carried out and has shown that the proposed semantic network program can be an effective component in modeling interaction between logical and pattern information processing.","PeriodicalId":350091,"journal":{"name":"2007 International Joint Conference on Neural Networks","volume":"34 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":"128982872","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: 1
Transfer Learning in Decision Trees 决策树中的迁移学习
2007 International Joint Conference on Neural Networks Pub Date : 2007-10-29 DOI: 10.1109/IJCNN.2007.4371047
Jun won Lee, C. Giraud-Carrier
{"title":"Transfer Learning in Decision Trees","authors":"Jun won Lee, C. Giraud-Carrier","doi":"10.1109/IJCNN.2007.4371047","DOIUrl":"https://doi.org/10.1109/IJCNN.2007.4371047","url":null,"abstract":"Most research in machine learning focuses on scenarios in which a learner faces a single learning task, independently of other learning tasks or prior knowledge. In reality, however, learning is not performed in isolation, starting from scratch with every new task. Instead, it is a lifelong activity during which a learner encounters many learning tasks, and usefully transfers to new tasks knowledge acquired from earlier related tasks. We propose a novel approach to transfer learning with decision trees. Our system learns a new task semi-incrementally from a partial decision tree model which captures knowledge from a previous task. Empirical results on several UCI data sets show that our approach is generally more effective and accurate than the base approach.","PeriodicalId":350091,"journal":{"name":"2007 International Joint Conference on Neural Networks","volume":"68 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":"125658366","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: 27
Application of Multi Objective Evolutionary Programming to Combined Economic Emission Dispatch Problem 多目标进化规划在组合经济排放调度问题中的应用
2007 International Joint Conference on Neural Networks Pub Date : 2007-10-29 DOI: 10.1109/IJCNN.2007.4371122
D. Jeyakumar, P. Venkatesh, Kwang Y. Lee
{"title":"Application of Multi Objective Evolutionary Programming to Combined Economic Emission Dispatch Problem","authors":"D. Jeyakumar, P. Venkatesh, Kwang Y. Lee","doi":"10.1109/IJCNN.2007.4371122","DOIUrl":"https://doi.org/10.1109/IJCNN.2007.4371122","url":null,"abstract":"This paper describes a new multi-objective evolutionary programming (MOEP) method to solve the combined economic emission dispatch (CEED) problem. CEED is a multi-objective optimization problem by considering the fuel cost and emission as the objectives. It is converted into single objective optimization problem using weighted sum method. Hence the MOEP is proposed by employing the non-dominated solution ranking as selection mechanism for the bi-objective CEED problem. The developed algorithm is tested for a three-unit and a six-unit system. The results demonstrate the capabilities of the proposed approach to generate well-distributed Pareto optimal solutions of the multi-objective CEED problem in a single run.","PeriodicalId":350091,"journal":{"name":"2007 International Joint Conference on Neural Networks","volume":"3 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":"130736898","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: 33
The Trouble with Weight-Dependent STDP 权重依赖性STDP的问题
2007 International Joint Conference on Neural Networks Pub Date : 2007-10-29 DOI: 10.1109/IJCNN.2007.4371154
D. Standage, T. Trappenberg
{"title":"The Trouble with Weight-Dependent STDP","authors":"D. Standage, T. Trappenberg","doi":"10.1109/IJCNN.2007.4371154","DOIUrl":"https://doi.org/10.1109/IJCNN.2007.4371154","url":null,"abstract":"We fit a weight-dependent STDP rule to the classic data of Bi and Poo (1998), showing that this rule leads to slow learning in a simulation with an integrate-and-fire neuron. The slowness of learning is explained by an inequality between the range of initial weights in the data and the largest relative potentiation. We show that slow learning can be overcome with an increased learning rate, but that this approach leads to rapid forgetting in the presence of realistic levels of background spiking. Our study demonstrates that weight-dependent STDP rules, commonly used in neural simulations, have biologically unrealistic consequences. We discuss the implications of this finding for several interpretations of weight-dependent plasticity and STDP more generally, and recommend directions for further research.","PeriodicalId":350091,"journal":{"name":"2007 International Joint Conference on Neural Networks","volume":"109 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":"132402792","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: 7
A Spatial Domain Sigma-Delta Modulation via Discrete-Time Cellular Neural Networks 基于离散时间细胞神经网络的空间域Sigma-Delta调制
2007 International Joint Conference on Neural Networks Pub Date : 2007-10-29 DOI: 10.1109/IJCNN.2007.4371237
H. Aomori, T. Otake, N. Takahashi, Mamoru Tanaka
{"title":"A Spatial Domain Sigma-Delta Modulation via Discrete-Time Cellular Neural Networks","authors":"H. Aomori, T. Otake, N. Takahashi, Mamoru Tanaka","doi":"10.1109/IJCNN.2007.4371237","DOIUrl":"https://doi.org/10.1109/IJCNN.2007.4371237","url":null,"abstract":"In this paper, a novel spatial domain sigma-delta modulation using two-layered discrete-time cellular neural networks (DT-CNNs) is proposed. Since the nature of CNN dynamics with the output function which has two saturation regions is to binarize the input image, the dynamics has a capabilities for a digital image halftoning. In the proposed architecture, the nonlinear interpolative dynamics is exploited to obtain an optimal reconstruction image from the bilevel modulated image, and quantization noises are spatially distributed by the noise shaping property of the dynamics. The experimental results show a excellent reconstruction performance and capabilities of the CNN as a sigma-delta modulation.","PeriodicalId":350091,"journal":{"name":"2007 International Joint Conference on Neural Networks","volume":"2 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":"132008112","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: 1
A Novel Weighted LBG Algorithm for Neural Spike Compression 一种新的加权LBG神经脉冲压缩算法
2007 International Joint Conference on Neural Networks Pub Date : 2007-10-29 DOI: 10.1109/IJCNN.2007.4371245
Sudhir Rao, A. Paiva, J. Príncipe
{"title":"A Novel Weighted LBG Algorithm for Neural Spike Compression","authors":"Sudhir Rao, A. Paiva, J. Príncipe","doi":"10.1109/IJCNN.2007.4371245","DOIUrl":"https://doi.org/10.1109/IJCNN.2007.4371245","url":null,"abstract":"In this paper, we present a weighted Linde-Buzo-Gray algorithm (WLBG) as a powerful and efficient technique for compressing neural spike data. We compare this technique with the recently proposed self-organizing map with dynamic learning (SOM-DL) and the traditional SOM. A significant achievement of WLBG over SOM-DL is a 15 dB increase in the SNR of the spike data apart from having a compression ratio of 150 : 1. Being simple and extremely fast, this algorithm allows real-time implementation on DSP chips opening new opportunities in BMI applications.","PeriodicalId":350091,"journal":{"name":"2007 International Joint Conference on Neural Networks","volume":"47 18 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":"128036621","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: 9
Visualization and classification of graph-structured data: the case of the Enron dataset 图结构数据的可视化和分类:安然数据集的案例
2007 International Joint Conference on Neural Networks Pub Date : 2007-10-29 DOI: 10.1109/IJCNN.2007.4371181
C. Bouveyron, H. Chipman
{"title":"Visualization and classification of graph-structured data: the case of the Enron dataset","authors":"C. Bouveyron, H. Chipman","doi":"10.1109/IJCNN.2007.4371181","DOIUrl":"https://doi.org/10.1109/IJCNN.2007.4371181","url":null,"abstract":"Graph-structured networks are often used to represent relationships between persons in organizations or communities. In this paper we investigate the problem of learning a latent space representation of the data in which proximity in the latent space increases the likelihood of a social tie between the nodes. In addition, this latent space representation can be used to classify these data into homogeneous groups in order to identify, for instance, marginal communities of persons. We propose a Bayesian way to select both dimension of the latent space and number of groups. We apply our approach to the Enron dataset and we show interesting representation and clustering of individuals.","PeriodicalId":350091,"journal":{"name":"2007 International Joint Conference on Neural Networks","volume":"2006 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":"128867726","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: 1
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