Pub Date : 2017-08-01DOI: 10.1109/GSIS.2017.8077705
D. Luo, Min Liu
In view of dynamic multi-criteria decision-making problems, in which the criteria value of alternatives are three-parameters interval numbers and criteria exists different natural states, a dynamic grey stochastic multi-criteria decision-making method is proposed. According to the stationary distribution thought of Markov chain, the final state of natural possibility is obtained. The time information is concentrated by calculating the importance of time function. On this basis, the three-unit connection number of three-parameter interval number is defined, and the optimization models based on single to noise ratio theory are constructed and solved according to the interactive interference of alternatives. Then, the comprehensive criteria weights are given based on the compromise thought, and alternatives are sorted by comparing the values of satisfaction of single to noise. Finally, An illustrative example shows the feasibility and rationality of the proposed approach.
{"title":"Dynamic grey stochastic decision-making method based on Markov chain and “matching” thought","authors":"D. Luo, Min Liu","doi":"10.1109/GSIS.2017.8077705","DOIUrl":"https://doi.org/10.1109/GSIS.2017.8077705","url":null,"abstract":"In view of dynamic multi-criteria decision-making problems, in which the criteria value of alternatives are three-parameters interval numbers and criteria exists different natural states, a dynamic grey stochastic multi-criteria decision-making method is proposed. According to the stationary distribution thought of Markov chain, the final state of natural possibility is obtained. The time information is concentrated by calculating the importance of time function. On this basis, the three-unit connection number of three-parameter interval number is defined, and the optimization models based on single to noise ratio theory are constructed and solved according to the interactive interference of alternatives. Then, the comprehensive criteria weights are given based on the compromise thought, and alternatives are sorted by comparing the values of satisfaction of single to noise. Finally, An illustrative example shows the feasibility and rationality of the proposed approach.","PeriodicalId":425920,"journal":{"name":"2017 International Conference on Grey Systems and Intelligent Services (GSIS)","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126616409","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}
Pub Date : 2017-08-01DOI: 10.1109/GSIS.2017.8077693
Fanlin Meng, Tianhui Wang, Bing-jun Li
In order to improve the prediction accuracy of GM(1,1) this paper points out the disadvantages of using least square method to solve the parameters of model, attempts to use particle swarm optimization algorithm (PSO) to calculate the parameter of GM(1,1), introduces the stochastic strategy into PSO to endow the inertia weight of particle randomly, and then selects high-rising exponential sequence and low-rising exponential sequence to establish the improved GM(1,1), traditional GM(1,1) and DGM(1,1) to compare the fitting accuracy. In addition, the grey correlation analysis is used to measure the similarity between the fitting sequence and the original sequence of three models. The results show that: for the low-rising exponential sequence, the improved GM(1,1) is slightly better than traditional GM(1,1) and DGM(1,1); for the high-rising exponential sequence, the superiority of improved GM(1,1) is obviously higher than the other two models, especially the traditional GM(1,1); for these two types of sequences, the geometry of fitting sequence based on improved GM(1,1) is closer to the geometry of original sequence.
{"title":"The improved GM(1,1) based on PSO with stochastic weight","authors":"Fanlin Meng, Tianhui Wang, Bing-jun Li","doi":"10.1109/GSIS.2017.8077693","DOIUrl":"https://doi.org/10.1109/GSIS.2017.8077693","url":null,"abstract":"In order to improve the prediction accuracy of GM(1,1) this paper points out the disadvantages of using least square method to solve the parameters of model, attempts to use particle swarm optimization algorithm (PSO) to calculate the parameter of GM(1,1), introduces the stochastic strategy into PSO to endow the inertia weight of particle randomly, and then selects high-rising exponential sequence and low-rising exponential sequence to establish the improved GM(1,1), traditional GM(1,1) and DGM(1,1) to compare the fitting accuracy. In addition, the grey correlation analysis is used to measure the similarity between the fitting sequence and the original sequence of three models. The results show that: for the low-rising exponential sequence, the improved GM(1,1) is slightly better than traditional GM(1,1) and DGM(1,1); for the high-rising exponential sequence, the superiority of improved GM(1,1) is obviously higher than the other two models, especially the traditional GM(1,1); for these two types of sequences, the geometry of fitting sequence based on improved GM(1,1) is closer to the geometry of original sequence.","PeriodicalId":425920,"journal":{"name":"2017 International Conference on Grey Systems and Intelligent Services (GSIS)","volume":"149 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133387674","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}
Pub Date : 2017-08-01DOI: 10.1109/GSIS.2017.8077678
Yong Wei, Kefang Zeng
The concept of uniform incidence degree is given in the paper. It is proved that the uniform incidence degree and similarity incidence degree are disjoint. The conversion methods between them, how to construct uniform incidence degree by similarity incidence degree, how to construct similarity incidence degree by uniform incidence degree are discussed in the paper.
{"title":"Uniform incidence degree and its properties","authors":"Yong Wei, Kefang Zeng","doi":"10.1109/GSIS.2017.8077678","DOIUrl":"https://doi.org/10.1109/GSIS.2017.8077678","url":null,"abstract":"The concept of uniform incidence degree is given in the paper. It is proved that the uniform incidence degree and similarity incidence degree are disjoint. The conversion methods between them, how to construct uniform incidence degree by similarity incidence degree, how to construct similarity incidence degree by uniform incidence degree are discussed in the paper.","PeriodicalId":425920,"journal":{"name":"2017 International Conference on Grey Systems and Intelligent Services (GSIS)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114170853","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}
Pub Date : 2017-08-01DOI: 10.1109/GSIS.2017.8077731
Chenlong Li, Hong-sen Yan
A novel identification method is proposed based on the characteristics of nonlinear time-delay systems. To identify nonlinear time-delay systems, Multi-dimensional Taylor network which has the powerful nonlinear approximation capability is utilized firstly, and then the improved particle swarm optimization algorithm is used to optimize weight numbers of Multi-dimensional Taylor network. To verify the effectivity of the proposed method, back propagation method is introduced to compare, the experimental results show that the proposed method identifies nonlinear time-delay systems effectively and is superior to the back propagation method.
{"title":"Nonlinear time-delay system identification based on multi-dimensional taylor network and IPSO","authors":"Chenlong Li, Hong-sen Yan","doi":"10.1109/GSIS.2017.8077731","DOIUrl":"https://doi.org/10.1109/GSIS.2017.8077731","url":null,"abstract":"A novel identification method is proposed based on the characteristics of nonlinear time-delay systems. To identify nonlinear time-delay systems, Multi-dimensional Taylor network which has the powerful nonlinear approximation capability is utilized firstly, and then the improved particle swarm optimization algorithm is used to optimize weight numbers of Multi-dimensional Taylor network. To verify the effectivity of the proposed method, back propagation method is introduced to compare, the experimental results show that the proposed method identifies nonlinear time-delay systems effectively and is superior to the back propagation method.","PeriodicalId":425920,"journal":{"name":"2017 International Conference on Grey Systems and Intelligent Services (GSIS)","volume":"193 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125350783","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}
Pub Date : 2017-08-01DOI: 10.1109/GSIS.2017.8077737
Zhuo Zhang, Min Xu
In an open innovation environment, complex equipment is always developed within a collaborative development network. Existing researches on collaborative development are mainly based on methodology of non-weighted static network, and assume that innovative search is independent with knowledge transfer and knowledge creation. This paper, based on scale-free network, establishes a new model to more precisely describe the process and behaviors of collaborative development for complex equipment, which includes innovation search, knowledge transfer and knowledge creation. By using simulation modeling, this paper investigates the dynamic mechanism of knowledge growth in the collaborative network, and probes the impact of innovation search on the performance of the collaborative development network. It is found that different strategies of innovation search have almost the same evolution path in the collaborative development network, and a similar equilibrium will be reached no matter what kind of search strategy is used. There is an inverted U-shape relationship between the tie density and the average knowledge stock (AKS) in the collaborative network, whereas network relation strength is positively correlated with the AKS. It is proved that strategies of innovation search significantly influence the AKS and the level of knowledge innovation in collaborative development environment.
{"title":"Collaborative development of complex equipment: A network perspective","authors":"Zhuo Zhang, Min Xu","doi":"10.1109/GSIS.2017.8077737","DOIUrl":"https://doi.org/10.1109/GSIS.2017.8077737","url":null,"abstract":"In an open innovation environment, complex equipment is always developed within a collaborative development network. Existing researches on collaborative development are mainly based on methodology of non-weighted static network, and assume that innovative search is independent with knowledge transfer and knowledge creation. This paper, based on scale-free network, establishes a new model to more precisely describe the process and behaviors of collaborative development for complex equipment, which includes innovation search, knowledge transfer and knowledge creation. By using simulation modeling, this paper investigates the dynamic mechanism of knowledge growth in the collaborative network, and probes the impact of innovation search on the performance of the collaborative development network. It is found that different strategies of innovation search have almost the same evolution path in the collaborative development network, and a similar equilibrium will be reached no matter what kind of search strategy is used. There is an inverted U-shape relationship between the tie density and the average knowledge stock (AKS) in the collaborative network, whereas network relation strength is positively correlated with the AKS. It is proved that strategies of innovation search significantly influence the AKS and the level of knowledge innovation in collaborative development environment.","PeriodicalId":425920,"journal":{"name":"2017 International Conference on Grey Systems and Intelligent Services (GSIS)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123473089","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}
Pub Date : 2017-08-01DOI: 10.1109/GSIS.2017.8077730
Camelia Delcea, I. Bradea, Liviu-Adrian Cotfas, E. Scarlat
The present paper aims to simulate an online environment in which both agents and consumers meet and exchange information related to certain types of goods (regular soft or durable goods, luxury soft of durable goods) on the purpose of establishing the number of agents a company need to “place” in the online environment in order to influence or change a person's opinion. On this purpose, a series of variables are defined using grey numbers and determined using a questionnaire and face-to-face interviews to sales managers. Based on them, an agent-based model is proposed.
{"title":"Opinion influence in online social media environments — U grey system theory and agent-based modeling approach","authors":"Camelia Delcea, I. Bradea, Liviu-Adrian Cotfas, E. Scarlat","doi":"10.1109/GSIS.2017.8077730","DOIUrl":"https://doi.org/10.1109/GSIS.2017.8077730","url":null,"abstract":"The present paper aims to simulate an online environment in which both agents and consumers meet and exchange information related to certain types of goods (regular soft or durable goods, luxury soft of durable goods) on the purpose of establishing the number of agents a company need to “place” in the online environment in order to influence or change a person's opinion. On this purpose, a series of variables are defined using grey numbers and determined using a questionnaire and face-to-face interviews to sales managers. Based on them, an agent-based model is proposed.","PeriodicalId":425920,"journal":{"name":"2017 International Conference on Grey Systems and Intelligent Services (GSIS)","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125248796","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}
Pub Date : 2017-08-01DOI: 10.1109/GSIS.2017.8077669
Ming-li Hu, Lei Ding
In the incomplete information system, no matter which dominance relation is being used to construct a rough set model the results would contain uncertainty. Considering three influence factors include the amount of information, the weight of attribute and the degree of different objects, a new grey dominance relation are defined in this paper. On this basis, a rough decision model is set up. The concept of difference coefficient is defined and optimized in the new model. An example is given to illustrate the efficiency and feasibility of the model.
{"title":"Rough decision model based on a new grey dominance relation","authors":"Ming-li Hu, Lei Ding","doi":"10.1109/GSIS.2017.8077669","DOIUrl":"https://doi.org/10.1109/GSIS.2017.8077669","url":null,"abstract":"In the incomplete information system, no matter which dominance relation is being used to construct a rough set model the results would contain uncertainty. Considering three influence factors include the amount of information, the weight of attribute and the degree of different objects, a new grey dominance relation are defined in this paper. On this basis, a rough decision model is set up. The concept of difference coefficient is defined and optimized in the new model. An example is given to illustrate the efficiency and feasibility of the model.","PeriodicalId":425920,"journal":{"name":"2017 International Conference on Grey Systems and Intelligent Services (GSIS)","volume":"99 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120893487","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}
Pub Date : 2017-08-01DOI: 10.1109/GSIS.2017.8077722
Lin Ma, Fengying Nie
With the rapid development of cloud computing, the Internet of things, big data has become one of the hot spots of society, but also a major focus of applied research, made many service sectors to become data-driven organizations, which exploit the value of big data resources to gain competitive advantage. The meteorological department and the Internet online monitoring collected a large number of meteorological data, deeply develop meteorological data value by using big data analysis method, also by comparing the data and model analysis technology. Within this paper, a literature review, surveys and expert interviews were used in this research paradigm, it analyzed the meteorological observation data management service system, proposed a value-driven smart meteorological service model based on big data. The aim of this paper is to construct a value-driven smart meteorological big data service model that includes resource collaboration, technical collaboration, and service collaboration, optimize the meteorological forecast processes. This promotes meteorological related services products more fine, more accurate and longer forecast timeliness in meteorological disaster prevention and mitigation, public transportation, flood control and drought, agricultural production, transportation and other areas.
{"title":"A smart meteorological service model based on big Data: A value creation perspective","authors":"Lin Ma, Fengying Nie","doi":"10.1109/GSIS.2017.8077722","DOIUrl":"https://doi.org/10.1109/GSIS.2017.8077722","url":null,"abstract":"With the rapid development of cloud computing, the Internet of things, big data has become one of the hot spots of society, but also a major focus of applied research, made many service sectors to become data-driven organizations, which exploit the value of big data resources to gain competitive advantage. The meteorological department and the Internet online monitoring collected a large number of meteorological data, deeply develop meteorological data value by using big data analysis method, also by comparing the data and model analysis technology. Within this paper, a literature review, surveys and expert interviews were used in this research paradigm, it analyzed the meteorological observation data management service system, proposed a value-driven smart meteorological service model based on big data. The aim of this paper is to construct a value-driven smart meteorological big data service model that includes resource collaboration, technical collaboration, and service collaboration, optimize the meteorological forecast processes. This promotes meteorological related services products more fine, more accurate and longer forecast timeliness in meteorological disaster prevention and mitigation, public transportation, flood control and drought, agricultural production, transportation and other areas.","PeriodicalId":425920,"journal":{"name":"2017 International Conference on Grey Systems and Intelligent Services (GSIS)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129528644","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}
Pub Date : 2017-08-01DOI: 10.1109/GSIS.2017.8077695
Bin Fu, Lifeng Wu
The relationship between NOx emissions and the size of car ownership in Beijing is discussed. To accurately predict NOx emissions, the linear model with the principle of new information priority is proposed and its property is proved. The comparisons with different approaches for predicting NOx emissions indicate that the proposed model exhibits a higher forecasting performance.
{"title":"Forecasting NOx emissions in beijing considering the size of car ownership","authors":"Bin Fu, Lifeng Wu","doi":"10.1109/GSIS.2017.8077695","DOIUrl":"https://doi.org/10.1109/GSIS.2017.8077695","url":null,"abstract":"The relationship between NOx emissions and the size of car ownership in Beijing is discussed. To accurately predict NOx emissions, the linear model with the principle of new information priority is proposed and its property is proved. The comparisons with different approaches for predicting NOx emissions indicate that the proposed model exhibits a higher forecasting performance.","PeriodicalId":425920,"journal":{"name":"2017 International Conference on Grey Systems and Intelligent Services (GSIS)","volume":"518 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116247774","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}
Pub Date : 2017-08-01DOI: 10.1109/GSIS.2017.8077663
Zhigeng Fang, Qin Zhang, Jiajia Cai, Qian Hu, Sifeng Liu
Most systems in the real life are complex systems. In the study of complex systems, we tend to get highly uncertain information due to the dynamic and complexity of the cases and our knowledge limitations. Moreover, complex systems are compounded of many parts, and the methods of data collection and storage vary considerably. Hence, the data processed by complex systems are associated with multi-type uncertainty, such as randomness, fuzziness and greyness. To solve the problems of limitations in the conventional representation and operations of uncertain data, this paper proposes a new type of method to represent and operate multi-type uncertain data. Firstly, we fully analyze the features of probability numbers, fuzzy numbers, interval valued fuzzy numbers and grey numbers, and study their relation and commonness so as to propose the concept and representation methods of generalized standard grey numbers. Then, we continue to discuss issues such as the basic operations of generalized standard grey numbers, as well as norm models and value comparison, to suggest a new solution to the representation and operations of uncertain data in complex systems.
{"title":"On general standard grey number representation and operations for multi-type uncertain data","authors":"Zhigeng Fang, Qin Zhang, Jiajia Cai, Qian Hu, Sifeng Liu","doi":"10.1109/GSIS.2017.8077663","DOIUrl":"https://doi.org/10.1109/GSIS.2017.8077663","url":null,"abstract":"Most systems in the real life are complex systems. In the study of complex systems, we tend to get highly uncertain information due to the dynamic and complexity of the cases and our knowledge limitations. Moreover, complex systems are compounded of many parts, and the methods of data collection and storage vary considerably. Hence, the data processed by complex systems are associated with multi-type uncertainty, such as randomness, fuzziness and greyness. To solve the problems of limitations in the conventional representation and operations of uncertain data, this paper proposes a new type of method to represent and operate multi-type uncertain data. Firstly, we fully analyze the features of probability numbers, fuzzy numbers, interval valued fuzzy numbers and grey numbers, and study their relation and commonness so as to propose the concept and representation methods of generalized standard grey numbers. Then, we continue to discuss issues such as the basic operations of generalized standard grey numbers, as well as norm models and value comparison, to suggest a new solution to the representation and operations of uncertain data in complex systems.","PeriodicalId":425920,"journal":{"name":"2017 International Conference on Grey Systems and Intelligent Services (GSIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130295590","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}
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