2015 IEEE International Conference on Grey Systems and Intelligent Services (GSIS)最新文献_第8页

Performance evaluation system of logistics engineering practical teaching course in application oriented undergraduate based on rough set theory 基于粗糙集理论的应用型本科物流工程实践教学课程绩效评价体系

2015 IEEE International Conference on Grey Systems and Intelligent Services (GSIS) Pub Date : 2015-10-26 DOI: 10.1109/GSIS.2015.7301901

Zhang Liguo

引用次数: 2

A Novel Modeling Method of Grey Verhulst Model Based on Optimizing Initial Condition 基于初始条件优化的灰色Verhulst模型建模新方法

2015 IEEE International Conference on Grey Systems and Intelligent Services (GSIS) Pub Date : 2015-10-26 DOI: 10.1109/GSIS.2015.7301859

Shu Hui, Wang Wen-ping, Xiong Ping-ping

{"title":"A Novel Modeling Method of Grey Verhulst Model Based on Optimizing Initial Condition","authors":"Shu Hui, Wang Wen-ping, Xiong Ping-ping","doi":"10.1109/GSIS.2015.7301859","DOIUrl":"https://doi.org/10.1109/GSIS.2015.7301859","url":null,"abstract":"A novel method of optimizing the grey Verhulst model is proposed in this study to improve prediction precision. The weighted average of each component of the first-order accumulative generation operator (1-AGO) sequence on the original sequence is taken as initial value of the initial condition in a time response function. According to the principle of new information priority, this paper takes the respective components of unitization sequence of time ordinal sequence corresponding to the 1-AGO sequence as the weighted coefficients. The time parameter of the initial condition in a time response function is solved by making the average of the absolute values of relative errors minimum between the 1-AGO simulation sequence and 1-AGO sequence. Thereby, the grey Verhulst model based on optimizing the initial condition is built. Finally, the proposed optimal model and two other grey Verhulst models are used to simulate and predict the recycling rate of industrial water. The simulating effect of these three models is examined and the predicting precision is compared and analyzed. The result indicates that all of the three models are qualified residual models and the predicting effect of the optimized model proposed in this study is obviously better than those of the other two models.","PeriodicalId":246110,"journal":{"name":"2015 IEEE International Conference on Grey Systems and Intelligent Services (GSIS)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123279267","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 modeling method for GM(1,1) and its application based on a particular data feature 基于特定数据特征的GM(1,1)建模方法及其应用

2015 IEEE International Conference on Grey Systems and Intelligent Services (GSIS) Pub Date : 2015-10-26 DOI: 10.1109/GSIS.2015.7301858

Jun Liu, Xin-ping Xiao, Shu-hua Mao

引用次数: 0

Grey prediction of China grain production with TEI@I methodology 中国粮食生产的TEI@I灰色预测方法

2015 IEEE International Conference on Grey Systems and Intelligent Services (GSIS) Pub Date : 2015-10-26 DOI: 10.1109/GSIS.2015.7301864

Qiting Chen, Chao Zhang

引用次数: 5

Some kinds of nonlinear strengthening operators for predicting the output value of china's marine electric power industry 几种用于预测中国船舶电力工业产值的非线性强化算子

2015 IEEE International Conference on Grey Systems and Intelligent Services (GSIS) Pub Date : 2015-10-26 DOI: 10.1109/GSIS.2015.7301822

Yuhong Wang, Zhengxin Wang

{"title":"Some kinds of nonlinear strengthening operators for predicting the output value of china's marine electric power industry","authors":"Yuhong Wang, Zhengxin Wang","doi":"10.1109/GSIS.2015.7301822","DOIUrl":"https://doi.org/10.1109/GSIS.2015.7301822","url":null,"abstract":"An efficient way to deal with the time series prediction problem in grey perturbed systems is by constructing effective grey operators. Due to the fixed structures of existing strengthening operators, the action intensity of these operators cannot be effectively controlled. This study aims to propose some nonlinear strengthening operators with flexible structures to effectively control the action intensity of operators to raw data and obtain the optimum prediction accuracy. Based on the axiom system of grey operators, the power average strengthening operator (PASO), the geometric power average strengthening operator (GPASO), the weighted power average strengthening operator (WPASO), the weighted geometric power average strengthening operator (WGPASO) are each constructed by introducing variable parameters into the construction of strengthening operators. Moreover, the properties of these operators, the relationship between different variable parameters and the action intensity of these operators are studied. Finally, with the prediction of China's marine electric power industry output value as an example, the effectiveness and superiority of these strengthening operators is confirmed.","PeriodicalId":246110,"journal":{"name":"2015 IEEE International Conference on Grey Systems and Intelligent Services (GSIS)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127908341","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

Dynamic grey target decision making method with three-parameter grey numbers 三参数灰数动态灰目标决策方法

2015 IEEE International Conference on Grey Systems and Intelligent Services (GSIS) Pub Date : 2015-10-26 DOI: 10.1108/GS-09-2015-0059

Shuli Yan, Sifeng Liu, Xiaqing Liu

引用次数: 8

Grey relational analysis and natural language Processing 灰色关联分析与自然语言处理

2015 IEEE International Conference on Grey Systems and Intelligent Services (GSIS) Pub Date : 2015-08-18 DOI: 10.1109/GSIS.2015.7301838

Arjab Singh Khuman, Yingjie Yang, Sifeng Liu

{"title":"Grey relational analysis and natural language Processing","authors":"Arjab Singh Khuman, Yingjie Yang, Sifeng Liu","doi":"10.1109/GSIS.2015.7301838","DOIUrl":"https://doi.org/10.1109/GSIS.2015.7301838","url":null,"abstract":"This paper investigates validity of using grey relational analysis (GRA) for natural language processing (NLP). The domain of NLP is one associated with inherent vagueness and abstraction, with many sub-domains all invoking their own associated uncertainties. Regardless of the particularisation, the main objective is understanding and making sense of linguistic lexicon. The inferencing and understanding of sentiment from natural language has been investigated thoroughly, however, the use of grey system theory in conjunction with NLP has yet to be explored in any great detail. Ergo, an introductory investigation into the effectiveness of using GRA on and with regards to NLP. This paper describes the feasibility of using grey system methodologies and tools, specifically the use of grey incidence, to provide a means for analysis of a sequence's geometric curve. The use of GRA provides one with the ability to inspect and infer sequences of data. Using this notion and by having a sequence represented as an input stream, it can be correlated against possible output commands. The use of grey incidence for quantifying and evaluating the correlation between what is inputted, against what output it is most similar to, is novel and should provide an additional facet to grey system theory.","PeriodicalId":246110,"journal":{"name":"2015 IEEE International Conference on Grey Systems and Intelligent Services (GSIS)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122858768","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

Research on the GM(1,1)-Markov chain model of highway freight volume forecasting 公路货运量预测GM(1,1)-马尔可夫链模型研究

2015 IEEE International Conference on Grey Systems and Intelligent Services (GSIS) Pub Date : 2015-08-01 DOI: 10.1109/GSIS.2015.7301874

Liu Wei, Gao Chunyang, Zhang Shaocheng

引用次数: 3

An analysis of supply chain restructuring based on Big Data and mobile Internet —A case study of warehouse-type supermarkets 基于大数据和移动互联网的供应链重构分析——以仓储式超市为例

2015 IEEE International Conference on Grey Systems and Intelligent Services (GSIS) Pub Date : 2015-08-01 DOI: 10.1109/GSIS.2015.7301898

Lin Ma, Fengying Nie, Qian Lu

引用次数: 10

Image segmentation algorithm based on improved genetic algorithms and grey relational degree analysis 基于改进遗传算法和灰色关联度分析的图像分割算法

2015 IEEE International Conference on Grey Systems and Intelligent Services (GSIS) Pub Date : 2015-08-01 DOI: 10.1109/GSIS.2015.7301853

Gui Yufeng, Su Peng, Chen Xianqiao

引用次数: 1