A new method of optimizing technical plans and evaluating the vibration of rolling bearings is proposed. This method is based on grey system theory, and allows a few data of experimentations and an unknown probability distribution of the studied system. Experimental researches on the vibration of the tapered roller bearing 32308 indicate that some bugs of statistics can be conquered, and the very good effect can be obtained by the proposed method.
{"title":"Evaluation for Optimum Technical Plan of Rolling Bearing Evaluation for Optimum Technical Plan of Rolling Bearing","authors":"X. Xia, Zhong-yu Wang, Xiaoyang Chen, Yongzhen Zhang","doi":"10.30016/JGS.200606.0002","DOIUrl":"https://doi.org/10.30016/JGS.200606.0002","url":null,"abstract":"A new method of optimizing technical plans and evaluating the vibration of rolling bearings is proposed. This method is based on grey system theory, and allows a few data of experimentations and an unknown probability distribution of the studied system. Experimental researches on the vibration of the tapered roller bearing 32308 indicate that some bugs of statistics can be conquered, and the very good effect can be obtained by the proposed method.","PeriodicalId":50187,"journal":{"name":"Journal of Grey System","volume":"9 1","pages":"9-13"},"PeriodicalIF":1.6,"publicationDate":"2006-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70062257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2006-06-01DOI: 10.30016/JGS.200606.0006
M. Yeh, Haoxun Yang, Chia-Ting Chang
This study attempts to use the GreyART network to construct a financial distress prediction model. The inputs applied to the network are the historical data containing 18 different financial ratios of 54 healthy and 22 distressed Taiwan's listed electronic firms. In order to determine the best result the GreyART network can attain, a new performance index is developed. Simulation results show the one using 8 variables to generate only four clusters, 1 for healthy class and 3 for distressed class with corresponding classification hit rates of 94.12% and 93.55% for the training and test phases, respectively.
{"title":"GreyART Network for Financial Distress Prediction Problem","authors":"M. Yeh, Haoxun Yang, Chia-Ting Chang","doi":"10.30016/JGS.200606.0006","DOIUrl":"https://doi.org/10.30016/JGS.200606.0006","url":null,"abstract":"This study attempts to use the GreyART network to construct a financial distress prediction model. The inputs applied to the network are the historical data containing 18 different financial ratios of 54 healthy and 22 distressed Taiwan's listed electronic firms. In order to determine the best result the GreyART network can attain, a new performance index is developed. Simulation results show the one using 8 variables to generate only four clusters, 1 for healthy class and 3 for distressed class with corresponding classification hit rates of 94.12% and 93.55% for the training and test phases, respectively.","PeriodicalId":50187,"journal":{"name":"Journal of Grey System","volume":"9 1","pages":"43-49"},"PeriodicalIF":1.6,"publicationDate":"2006-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70055427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2006-06-01DOI: 10.30016/JGS.200606.0001
H. Tsai, B. Chang
Grey model (GM) has encountered the crucial problem of overshoot when applying to the non-periodic short-term prediction. At the same period, cumulated 3-point least squared linear prediction (C3LSP) alternatively confronts the opposite situation, i.e. underestimation. Nevertheless, a method of combining both preceding models is proposed for resolving the overshoot and underestimation phenomena significantly that is hybrid BPNN-weighted GREY-C3LSP prediction (BWGC) model. However, some predicted outcomes resulted from BWGC are not accurate enough as few observations deviate far away from both GM and C3LSP outputs. Thus, compensation is figured out to deal with the time-varying variance of the residuals in BWGC. That is, incorporating a non-linear generalized autoregressive conditional heteroscedasticity (NGARCH) into BWGC is applied, and then adaptive support vector regression (ASVR) is employed for tuning the appropriate coefficients for both BWGC and NGARCH to effectively improve the predictive accuracy.
{"title":"Adaptive Support Vector Regression Tuning Composite Model of BWCG and NGARCH for Applications of Time-Series Prediction","authors":"H. Tsai, B. Chang","doi":"10.30016/JGS.200606.0001","DOIUrl":"https://doi.org/10.30016/JGS.200606.0001","url":null,"abstract":"Grey model (GM) has encountered the crucial problem of overshoot when applying to the non-periodic short-term prediction. At the same period, cumulated 3-point least squared linear prediction (C3LSP) alternatively confronts the opposite situation, i.e. underestimation. Nevertheless, a method of combining both preceding models is proposed for resolving the overshoot and underestimation phenomena significantly that is hybrid BPNN-weighted GREY-C3LSP prediction (BWGC) model. However, some predicted outcomes resulted from BWGC are not accurate enough as few observations deviate far away from both GM and C3LSP outputs. Thus, compensation is figured out to deal with the time-varying variance of the residuals in BWGC. That is, incorporating a non-linear generalized autoregressive conditional heteroscedasticity (NGARCH) into BWGC is applied, and then adaptive support vector regression (ASVR) is employed for tuning the appropriate coefficients for both BWGC and NGARCH to effectively improve the predictive accuracy.","PeriodicalId":50187,"journal":{"name":"Journal of Grey System","volume":"59 1","pages":"1-7"},"PeriodicalIF":1.6,"publicationDate":"2006-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70062214","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2005-12-01DOI: 10.30016/JGS.200512.0003
M. Nagai, D. Yamaguchi, GuoDong Li
This paper proposes a new system modeling approach about Grey Structural Modeling (GSM) which is based on grey theory, and it just likes ISM and FSM. Each class of systems depends on the order of localized grey relational grade. And each path of elements is found by the ordered pair of according to globalized grey relational grade. Classes and paths are both used Yamaguchi's grey relational grade, since it is more suitable to find the topology of given elements. GSM draws directed graph (digraph) by using three parameters: distinguish coefficient ζ which decides the basic composition of digraph, class coefficient θ which gives the hierarchy, and path coefficient ψ which gives an ordered pair of elements arrow. It is possible that the GSM handles not only causal binary relation, but also observed value which causality is unknown. Three examples are given, proposal method is analyzed and compared with traditional methods.
{"title":"Grey Structural Modeling","authors":"M. Nagai, D. Yamaguchi, GuoDong Li","doi":"10.30016/JGS.200512.0003","DOIUrl":"https://doi.org/10.30016/JGS.200512.0003","url":null,"abstract":"This paper proposes a new system modeling approach about Grey Structural Modeling (GSM) which is based on grey theory, and it just likes ISM and FSM. Each class of systems depends on the order of localized grey relational grade. And each path of elements is found by the ordered pair of according to globalized grey relational grade. Classes and paths are both used Yamaguchi's grey relational grade, since it is more suitable to find the topology of given elements. GSM draws directed graph (digraph) by using three parameters: distinguish coefficient ζ which decides the basic composition of digraph, class coefficient θ which gives the hierarchy, and path coefficient ψ which gives an ordered pair of elements arrow. It is possible that the GSM handles not only causal binary relation, but also observed value which causality is unknown. Three examples are given, proposal method is analyzed and compared with traditional methods.","PeriodicalId":50187,"journal":{"name":"Journal of Grey System","volume":"8 1","pages":"119-130"},"PeriodicalIF":1.6,"publicationDate":"2005-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70062006","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2005-12-01DOI: 10.30016/JGS.200512.0005
L. Tsai, Horng-Yith Liou, Guang-Fu Jiang
This paper is aimed at the utilization of grey relational analysis model to investigate the influential factors, i.e., the helical angle, coil diameter, wire diameter, and the number of active coils, on the dynamic properties on the dynamic properties of a helical spring during manufacturing. The natural frequencies are selected as the quality targets in the present research. With a view to bypassing the tedious laboratory task in measuring the natural frequencies related to these 81 combinations of those influential factors, a finite-element model with the aid of ANSYS package is established for the computer simulation instead. Of the four significant variables under investigation, the helical angle is found to be the most influential one for the first four natural frequencies, two for longitudinal modes, and two for bending modes, respectively. The method propounded in this paper may serve the purpose of providing the researchers with an alternative approach to some other related applications.
{"title":"Application of Grey Relational Analysis to the Influential Factors on Natural Frequencies of Helical Springs","authors":"L. Tsai, Horng-Yith Liou, Guang-Fu Jiang","doi":"10.30016/JGS.200512.0005","DOIUrl":"https://doi.org/10.30016/JGS.200512.0005","url":null,"abstract":"This paper is aimed at the utilization of grey relational analysis model to investigate the influential factors, i.e., the helical angle, coil diameter, wire diameter, and the number of active coils, on the dynamic properties on the dynamic properties of a helical spring during manufacturing. The natural frequencies are selected as the quality targets in the present research. With a view to bypassing the tedious laboratory task in measuring the natural frequencies related to these 81 combinations of those influential factors, a finite-element model with the aid of ANSYS package is established for the computer simulation instead. Of the four significant variables under investigation, the helical angle is found to be the most influential one for the first four natural frequencies, two for longitudinal modes, and two for bending modes, respectively. The method propounded in this paper may serve the purpose of providing the researchers with an alternative approach to some other related applications.","PeriodicalId":50187,"journal":{"name":"Journal of Grey System","volume":"8 1","pages":"141-155"},"PeriodicalIF":1.6,"publicationDate":"2005-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70062488","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2005-12-01DOI: 10.30016/JGS.200512.0006
Meng Lu, K. Wevers, R. V. D. Heijden, V. Marchau
Both large-scale physical infrastructure redesign and extensive use of in- vehicle driving assistance systems can contribute to improving road traffic safety. Limited availability of effect data (historical and estimated) for both alternatives is hampering long-term strategic analysis of their potential effects. This paper investigates the use of a first-order and one- variable grey model, denoted as GM (1,1), to forecast the trend of the reduction of traffic accident severity (in terms of fatalities and hospitalisations) through mentioned strategies and combinations thereof. Based on modelling the limited available data of the effects of the infrastructure redesign programme in The Netherlands for the period 1998-2002, we forecast the trend of fatalities and hospitalisations for the years 2003 until 2010. The result is compared with other traffic safety enhancement scenarios by using cost-effectiveness analysis (CEA). Error analysis shows that the applied model has a high degree of reliability. Therefore, the method (grey model and CEA) and the outcome of the analysis may contribute to planning and decision making concerning further appropriate steps to reach the ambitious Dutch road traffic safety goals for 2010.
{"title":"Modelling and Forecasting Traffic Safety Improvement: Infrastructure Redesign Vs Driving Assistance Systems","authors":"Meng Lu, K. Wevers, R. V. D. Heijden, V. Marchau","doi":"10.30016/JGS.200512.0006","DOIUrl":"https://doi.org/10.30016/JGS.200512.0006","url":null,"abstract":"Both large-scale physical infrastructure redesign and extensive use of in- vehicle driving assistance systems can contribute to improving road traffic safety. Limited availability of effect data (historical and estimated) for both alternatives is hampering long-term strategic analysis of their potential effects. This paper investigates the use of a first-order and one- variable grey model, denoted as GM (1,1), to forecast the trend of the reduction of traffic accident severity (in terms of fatalities and hospitalisations) through mentioned strategies and combinations thereof. Based on modelling the limited available data of the effects of the infrastructure redesign programme in The Netherlands for the period 1998-2002, we forecast the trend of fatalities and hospitalisations for the years 2003 until 2010. The result is compared with other traffic safety enhancement scenarios by using cost-effectiveness analysis (CEA). Error analysis shows that the applied model has a high degree of reliability. Therefore, the method (grey model and CEA) and the outcome of the analysis may contribute to planning and decision making concerning further appropriate steps to reach the ambitious Dutch road traffic safety goals for 2010.","PeriodicalId":50187,"journal":{"name":"Journal of Grey System","volume":"8 1","pages":"157-166"},"PeriodicalIF":1.6,"publicationDate":"2005-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70062032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2005-12-01DOI: 10.30016/JGS.200512.0007
D. Yamaguchi, GuoDong Li, M. Nagai
Grey relational analysis is a useful method in many fields as well known, and is presented variously, since the distinguish coefficient is fixed on using. This paper proposes a new approach for grey relational analysis that is based on topological background, and this proposal method has 3 features: (1) Grey relational matrix is always symmetric, (2) Order relation of with given samples is followed grey relational grade, (3) Distinguish coefficient is able to find an invariable structure of given data set. Two simulation examples are given, such as IRIS data set and WINE data set. This proposal method is compared with traditional grey relational analysis, about metric calculation and grey relational characteristic. In addition, it has obtained the order of given samples more clearly. And three examples are also given to show how to use the proposal method and distinguish coefficient in pattern recognition, information retrieval, and kansei engineering.
{"title":"New Grey Relational Analysis for Finding the Invariable Structure and Its Applications","authors":"D. Yamaguchi, GuoDong Li, M. Nagai","doi":"10.30016/JGS.200512.0007","DOIUrl":"https://doi.org/10.30016/JGS.200512.0007","url":null,"abstract":"Grey relational analysis is a useful method in many fields as well known, and is presented variously, since the distinguish coefficient is fixed on using. This paper proposes a new approach for grey relational analysis that is based on topological background, and this proposal method has 3 features: (1) Grey relational matrix is always symmetric, (2) Order relation of with given samples is followed grey relational grade, (3) Distinguish coefficient is able to find an invariable structure of given data set. Two simulation examples are given, such as IRIS data set and WINE data set. This proposal method is compared with traditional grey relational analysis, about metric calculation and grey relational characteristic. In addition, it has obtained the order of given samples more clearly. And three examples are also given to show how to use the proposal method and distinguish coefficient in pattern recognition, information retrieval, and kansei engineering.","PeriodicalId":50187,"journal":{"name":"Journal of Grey System","volume":"8 1","pages":"167-178"},"PeriodicalIF":1.6,"publicationDate":"2005-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70062132","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2005-12-01DOI: 10.30016/JGS.200512.0004
S. Wan, Ting-Cheng Chang
Water quality for life on earth can hardly be underestimated. One of the ways to maintain water quality is to develop an effective water management strategy. The goal of this paper provides a feasible solution for evaluation the quality of dams. The entire study can be broken into two stages. In the first stage, it is decided to analyze the importance of input parameters. A hybrid model of considering a back propagation neural network with EDBD algorithm will be used to find the Relative Importance of each input parameter. Then, Relative Importance was considered as different weights. In the second stage, the grey relational analysis and those weights were used to find the behavior of quality on the reservoirs.
{"title":"The Application of a Hybrid Model of EDBD Algorithms and Grey Relational Analysis: A Solution for Knockout Poor Quality of Water Reservoirs","authors":"S. Wan, Ting-Cheng Chang","doi":"10.30016/JGS.200512.0004","DOIUrl":"https://doi.org/10.30016/JGS.200512.0004","url":null,"abstract":"Water quality for life on earth can hardly be underestimated. One of the ways to maintain water quality is to develop an effective water management strategy. The goal of this paper provides a feasible solution for evaluation the quality of dams. The entire study can be broken into two stages. In the first stage, it is decided to analyze the importance of input parameters. A hybrid model of considering a back propagation neural network with EDBD algorithm will be used to find the Relative Importance of each input parameter. Then, Relative Importance was considered as different weights. In the second stage, the grey relational analysis and those weights were used to find the behavior of quality on the reservoirs.","PeriodicalId":50187,"journal":{"name":"Journal of Grey System","volume":"8 1","pages":"131-139"},"PeriodicalIF":1.6,"publicationDate":"2005-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70062409","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2005-12-01DOI: 10.30016/JGS.200512.0002
Kun-Chieh Wang
The thermal effect on machine tools has become a well-recognized problem in response to the increasing requirement of product quality. The performance of a thermal error compensation system strongly depends on the accuracy of the thermal error model. To establish the compensation model of the thermal error of a CNC two-turret lathe, the methods of the grey theory (GT), feed-forward neural network (FNN), radial basis function neural network (RBFNN), and generalized regression neural network (GRNN) were used. Results found by the grey theory showed that the characteristic temperature rise at the spindle nose is the most important factor influencing the thermal deformation. Comparisons among all mentioned neural network models showed that the RBFNN model has the best ability to map the thermal drift to temperature ascent of the machine structure.
{"title":"Grey Theory and Radial Basis Function Neural Network Applied to Thermal Error Compensation in a CNC Lathe","authors":"Kun-Chieh Wang","doi":"10.30016/JGS.200512.0002","DOIUrl":"https://doi.org/10.30016/JGS.200512.0002","url":null,"abstract":"The thermal effect on machine tools has become a well-recognized problem in response to the increasing requirement of product quality. The performance of a thermal error compensation system strongly depends on the accuracy of the thermal error model. To establish the compensation model of the thermal error of a CNC two-turret lathe, the methods of the grey theory (GT), feed-forward neural network (FNN), radial basis function neural network (RBFNN), and generalized regression neural network (GRNN) were used. Results found by the grey theory showed that the characteristic temperature rise at the spindle nose is the most important factor influencing the thermal deformation. Comparisons among all mentioned neural network models showed that the RBFNN model has the best ability to map the thermal drift to temperature ascent of the machine structure.","PeriodicalId":50187,"journal":{"name":"Journal of Grey System","volume":"8 1","pages":"107-118"},"PeriodicalIF":1.6,"publicationDate":"2005-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70055367","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2005-12-01DOI: 10.30016/JGS.200512.0001
Hsiao-Chi Chen, Yi-Chung Hu, J. Z. Shyu, G. Tzeng
Causality and time series model are the most effective methods used in forecasting practices. Time series models, such as ARIMA, are used by most researchers in stock price prediction. However, in the financial environment, the information on the stock market is vague. To solve this problem, this work presents two forecasting models to help investors make decisions in stock market: one is a new model named possibility grey forecasting model, and the other is the neural network-based fuzzy regression. Moreover, the differences between them and the scenarios for implementing them are also analyzed in this paper to help investors to plan their own investment strategies under various conditions. In the empirical study, we demonstrate that the proposed method and the neural network-based fuzzy regression can be used to effectively find the stock index in Taiwan.
{"title":"Comparing Possibility Grey Forecasting with Neural network-based Fuzzy Regression by an Empirical Study","authors":"Hsiao-Chi Chen, Yi-Chung Hu, J. Z. Shyu, G. Tzeng","doi":"10.30016/JGS.200512.0001","DOIUrl":"https://doi.org/10.30016/JGS.200512.0001","url":null,"abstract":"Causality and time series model are the most effective methods used in forecasting practices. Time series models, such as ARIMA, are used by most researchers in stock price prediction. However, in the financial environment, the information on the stock market is vague. To solve this problem, this work presents two forecasting models to help investors make decisions in stock market: one is a new model named possibility grey forecasting model, and the other is the neural network-based fuzzy regression. Moreover, the differences between them and the scenarios for implementing them are also analyzed in this paper to help investors to plan their own investment strategies under various conditions. In the empirical study, we demonstrate that the proposed method and the neural network-based fuzzy regression can be used to effectively find the stock index in Taiwan.","PeriodicalId":50187,"journal":{"name":"Journal of Grey System","volume":"8 1","pages":"93-106"},"PeriodicalIF":1.6,"publicationDate":"2005-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70055189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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