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2008 Second International Conference on Genetic and Evolutionary Computing最新文献

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An Improved System Cloud Grey Neural Network Model 改进的系统云灰色神经网络模型
2008 Second International Conference on Genetic and Evolutionary Computing Pub Date : 2008-10-03 DOI: 10.1109/WGEC.2008.62
Sikun Yang
{"title":"An Improved System Cloud Grey Neural Network Model","authors":"Sikun Yang","doi":"10.1109/WGEC.2008.62","DOIUrl":"https://doi.org/10.1109/WGEC.2008.62","url":null,"abstract":"This paper improved and optimized the topology structure of the system cloud grey neural network model (SCGNNM (1,1)) and presented a novel SCGNNM (1,1) based on time response model. Because the dispersed data of time response model can be regarded as the data abstracted from the continued function, the model's precision can be improved greatly. Meantime, the learning algorithm is given. Finally, the proposed model is simulated and shown to be very reliable.","PeriodicalId":198475,"journal":{"name":"2008 Second International Conference on Genetic and Evolutionary Computing","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127341654","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
Study on Key Technologies and Software Design of BACnet Router BACnet路由器关键技术及软件设计研究
2008 Second International Conference on Genetic and Evolutionary Computing Pub Date : 2008-09-25 DOI: 10.1109/WGEC.2008.102
Quan Liu, P. Ren
{"title":"Study on Key Technologies and Software Design of BACnet Router","authors":"Quan Liu, P. Ren","doi":"10.1109/WGEC.2008.102","DOIUrl":"https://doi.org/10.1109/WGEC.2008.102","url":null,"abstract":"BACnet (Building Automation and Control networks) is a standard data communication protocol for building automation and control systems. The purpose of the BACnet network layer is to provide the means by which messages can be transmitted from one BACnet network to another regardless of the BACnet data link technology in use on that network. BACnet routers are devices that interconnect two or more BACnet networks to form a BACnet internet work and route messages between BACnet networks. In this study, the authors analyze the key technologies of BACnet router, propose a new algorithm named PRED to solve the congestion control problem of BACnet router and a software design program to put BACnet router into practice.","PeriodicalId":198475,"journal":{"name":"2008 Second International Conference on Genetic and Evolutionary Computing","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125047609","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
Forecasting of Government's Financial Educational Fund by Using Neural Networks Model 基于神经网络模型的政府财政教育经费预测
2008 Second International Conference on Genetic and Evolutionary Computing Pub Date : 2008-09-25 DOI: 10.1109/WGEC.2008.129
Kai Li
{"title":"Forecasting of Government's Financial Educational Fund by Using Neural Networks Model","authors":"Kai Li","doi":"10.1109/WGEC.2008.129","DOIUrl":"https://doi.org/10.1109/WGEC.2008.129","url":null,"abstract":"Forecasting method using neural networks has been advocated as an alternative to traditional statistical forecasting in recent years. The paper built a feed-forward neural network model to forecast the values of governmentpsilas financial educational fund (GFEF) in year 2010. On the basis of data processing, the structure of neural networks was given. The algorithm that adopted as a learning phase in the model was a fast one differing from that of the steep decent algorithm. The forecasts obtained from neural networks model were compared with the data forecasting by experts, and the error curve and the auto-adjusting curve of learning rate were also illustrated. The results show that the model was very effective.","PeriodicalId":198475,"journal":{"name":"2008 Second International Conference on Genetic and Evolutionary Computing","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121886846","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
An Improved Face Recognition Method Based on Filled Function 一种基于填充函数的改进人脸识别方法
2008 Second International Conference on Genetic and Evolutionary Computing Pub Date : 2008-09-25 DOI: 10.1109/WGEC.2008.38
Shenghui Wang, Yingtao Xu, Bo Zhu
{"title":"An Improved Face Recognition Method Based on Filled Function","authors":"Shenghui Wang, Yingtao Xu, Bo Zhu","doi":"10.1109/WGEC.2008.38","DOIUrl":"https://doi.org/10.1109/WGEC.2008.38","url":null,"abstract":"3D data registration and classifier are two important components in face recognition system. Aiming at the handicaps in current methods such as slow convergence or easiness of getting into local optimization, this paper works out a novel face recognition method combining filled function method, which can find a lower local minimizer by leaving the local minimizer previously found. By repeating these processes, a global minimizer can be obtained at last. Then it works out an improved ICP 3D data registration algorithm and an improved BP neural network classifier. Experiments show that this face recognition method decreases the amount of calculation, improves the accuracy of recognition precision and has an actual recognition effect.","PeriodicalId":198475,"journal":{"name":"2008 Second International Conference on Genetic and Evolutionary Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128561395","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
Study on Improved Fast Immunized Genetic Algorithm 改进的快速免疫遗传算法研究
2008 Second International Conference on Genetic and Evolutionary Computing Pub Date : 2008-09-25 DOI: 10.1109/WGEC.2008.67
Wei Gao
{"title":"Study on Improved Fast Immunized Genetic Algorithm","authors":"Wei Gao","doi":"10.1109/WGEC.2008.67","DOIUrl":"https://doi.org/10.1109/WGEC.2008.67","url":null,"abstract":"As an effective global optimization method, genetic algorithm has been used in real practice very widely. When it is used in real practice, its slow convergence and poor stability have become the main problems. In order to overcome these problems, from the creation of the initial population, immune selection operation, improved genetic operators, et al, an improved fast immunized genetic algorithm is proposed. Through the simulation experiments of some hard-optimization functions, the proposed algorithm shows its faster convergence and better stability than a lot of existing algorithms'.","PeriodicalId":198475,"journal":{"name":"2008 Second International Conference on Genetic and Evolutionary Computing","volume":"142 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124690237","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
Optimization for Cyclosporine Blood Concentration Prediction Based on Genetic Algorithm - BP Neural Network 基于遗传算法- BP神经网络的环孢素血药浓度预测优化
2008 Second International Conference on Genetic and Evolutionary Computing Pub Date : 2008-09-25 DOI: 10.1109/WGEC.2008.99
Shan Li, Haibing Chen, Junxian Yun, LiuZheng Zhou, Jie Xia, Jiong Zhang, Y. Yin
{"title":"Optimization for Cyclosporine Blood Concentration Prediction Based on Genetic Algorithm - BP Neural Network","authors":"Shan Li, Haibing Chen, Junxian Yun, LiuZheng Zhou, Jie Xia, Jiong Zhang, Y. Yin","doi":"10.1109/WGEC.2008.99","DOIUrl":"https://doi.org/10.1109/WGEC.2008.99","url":null,"abstract":"The paper proposes the new model of the genetic algorithm and BP neural networks to predict the blood concentration of Cyclosporine. The BP model was optimized by genetic algorithm to overcome the slower convergence speed, and the best result was found in the particular condition with the strong search function of genetic algorithm. The prediction precision of average blood concentration of Cyclosporine by using the GABP neural network model was 97.5%. It was found that the GABP model was superior to BP neural network in the prediction of Cyclosporine blood concentration. We conclude the GABP model can be used in the prediction of the Cyclosporine blood concentration and help clinicians provide better care for patients taking CsA.","PeriodicalId":198475,"journal":{"name":"2008 Second International Conference on Genetic and Evolutionary Computing","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123862391","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 3D Web GIS System Based on VRML and X3D 基于VRML和X3D的三维Web GIS系统
2008 Second International Conference on Genetic and Evolutionary Computing Pub Date : 2008-09-25 DOI: 10.1109/WGEC.2008.6
Wang Ming
{"title":"A 3D Web GIS System Based on VRML and X3D","authors":"Wang Ming","doi":"10.1109/WGEC.2008.6","DOIUrl":"https://doi.org/10.1109/WGEC.2008.6","url":null,"abstract":"Web GIS is experiencing the development from 2D system to 3D system, and the Web GIS based on VRML is the most popular form of the 3D Web GIS. By analyzing the existing Web GIS system based on VRML, this paper points out its disadvantages and introduces the next generation standard for Web3D - X3D(Extensible 3D specification). A Web GIS model based on X3D is also put forward, which has its own advantages that VRML is incomparable with: good rehostability; easy realization of big scene graphs and integration of heterogeneous database, spatial data sharing and its mutual operation etc.","PeriodicalId":198475,"journal":{"name":"2008 Second International Conference on Genetic and Evolutionary Computing","volume":"142 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115854422","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
A Non-cooperative Power Control Algorithm for Wireless Ad Hoc and Sensor Networks 无线自组网和传感器网络的非合作功率控制算法
2008 Second International Conference on Genetic and Evolutionary Computing Pub Date : 2008-09-25 DOI: 10.1109/WGEC.2008.95
Qiang Sun, Xian-quan Zeng, Niansheng Chen, Zongwu Ke, R. ur Rasool
{"title":"A Non-cooperative Power Control Algorithm for Wireless Ad Hoc and Sensor Networks","authors":"Qiang Sun, Xian-quan Zeng, Niansheng Chen, Zongwu Ke, R. ur Rasool","doi":"10.1109/WGEC.2008.95","DOIUrl":"https://doi.org/10.1109/WGEC.2008.95","url":null,"abstract":"In mobile ad hoc & sensor networks, power control is an efficient way to improve the efficiency of energy. However, it will also produce negative influence on many aspects of the networks such as the network connectivity, delay and network capacity. In this paper, proposed related energy control mechanisms for ad hoc & sensor networks are introduced. Considering the QoS requirement such as maximum network capacity, minimum network radius and guaranteed network connectivity, a power control model based on non-cooperative game theory is given. And a distributed non-cooperative game algorithm to power control for ad hoc & sensor networks is presented. The existing and uniqueness of Nash equilibrium for the algorithm is also proved in this paper. Finally simulation experiments are implemented to verify the algorithm. The results show that the algorithm is efficient and has a good integrated performance.","PeriodicalId":198475,"journal":{"name":"2008 Second International Conference on Genetic and Evolutionary Computing","volume":"488 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133971065","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
Research on Improved Data-Mining Algorithm Based on Strong Correlation 基于强相关的改进数据挖掘算法研究
2008 Second International Conference on Genetic and Evolutionary Computing Pub Date : 2008-09-25 DOI: 10.1109/WGEC.2008.119
Chun-hong Hu, Zhengqiang Wang
{"title":"Research on Improved Data-Mining Algorithm Based on Strong Correlation","authors":"Chun-hong Hu, Zhengqiang Wang","doi":"10.1109/WGEC.2008.119","DOIUrl":"https://doi.org/10.1109/WGEC.2008.119","url":null,"abstract":"The extensive application of association rules in commerce enables itself to be one of the most active research directions in data mining. Recently, the mining of strong correlation item pairs with statistical significance in transaction database receives a certain value. In order to further reduce the cost of testing candidate item pairs in relational database, we have developed the Taper algorithm according to 1NF property. The developed TaperR algorithm can cut the number of candidate pairs to improve effciency.","PeriodicalId":198475,"journal":{"name":"2008 Second International Conference on Genetic and Evolutionary Computing","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134086499","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
A Grid and Density-Based Clustering Algorithm for Processing Data Stream 一种基于网格和密度的数据流聚类算法
2008 Second International Conference on Genetic and Evolutionary Computing Pub Date : 2008-09-25 DOI: 10.1109/WGEC.2008.32
Chenke Jia, Chen Tan, Ai Yong
{"title":"A Grid and Density-Based Clustering Algorithm for Processing Data Stream","authors":"Chenke Jia, Chen Tan, Ai Yong","doi":"10.1109/WGEC.2008.32","DOIUrl":"https://doi.org/10.1109/WGEC.2008.32","url":null,"abstract":"This paper proposes DD-Stream, a framework for density-based clustering stream data. The algorithm adopts a density decaying technique to capture the evolving data stream and extracts the boundary point of grid by the DCQ-means algorithm. Our method resolving the problem of evolving automatic clustering of real-time data streams, cannot only find arbitrary shaped clusters with noise, but also avoid the clustering quality problems caused by discarding the boundary point of grid, our algorithm has better scalability in processing large-scale and high dimensional stream data as well.","PeriodicalId":198475,"journal":{"name":"2008 Second International Conference on Genetic and Evolutionary Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131891205","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: 61
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