The attribute recognition model (ARM) has been widely used to make comprehensive assessment in many engineering fields, such as environment, ecology, and economy. However, large numbers of experiments indicate that th...The attribute recognition model (ARM) has been widely used to make comprehensive assessment in many engineering fields, such as environment, ecology, and economy. However, large numbers of experiments indicate that the value of weight vector has no relativity to its initial value but depends on the data of Quality Standard and actual samples. In the present study, the ARM is enhanced with the technique of data driving, which means some more groups of data from the Quality Standard are selected with the uniform random method to make the calculation of weight values more rational and more scientific. This improved attribute recognition model (IARM) is applied to a real case of assessment on seawater quality. The given example shows that the IARM has the merits of being simple in principle, easy to operate, and capable of producing objective results, and is therefore of use in evaluation problems in marine environment science.展开更多
In recent years, methods of fuzzy comprehensive assessment (FCA) have been widely applied in environmental quality comprehensive assessment(EQCA) without conditions. A lot of practised theses have been published. They...In recent years, methods of fuzzy comprehensive assessment (FCA) have been widely applied in environmental quality comprehensive assessment(EQCA) without conditions. A lot of practised theses have been published. They imitate each other and have a great influence. In this paper, however, opposite examples are given and we further prove that using environmental quality fuzzy comprehensive assessment method (EQFCAM) without conditions will lead to the absolutely non-scientific result. Two basic rules that FCA should adhere to are put forward in this paper. They should be the foundation of EQCA. The sufficient and necessary condition that should meet the fundamental rules in methods of FCA will also be given.展开更多
基金The authors would like to acknowledge the funding support of the National Natural Science Foundation of China （50579009, 70471090） the National 10 th Five Year Scientific Project of China for Tackling the Key Problems （2004BA608B-02 - 02） and the Excellence Youth Teacher Sustentation Fund Program of the Ministry of Education of China （Department of Education and Personnel [2002] 350）.
文摘The attribute recognition model (ARM) has been widely used to make comprehensive assessment in many engineering fields, such as environment, ecology, and economy. However, large numbers of experiments indicate that the value of weight vector has no relativity to its initial value but depends on the data of Quality Standard and actual samples. In the present study, the ARM is enhanced with the technique of data driving, which means some more groups of data from the Quality Standard are selected with the uniform random method to make the calculation of weight values more rational and more scientific. This improved attribute recognition model (IARM) is applied to a real case of assessment on seawater quality. The given example shows that the IARM has the merits of being simple in principle, easy to operate, and capable of producing objective results, and is therefore of use in evaluation problems in marine environment science.
文摘In recent years, methods of fuzzy comprehensive assessment (FCA) have been widely applied in environmental quality comprehensive assessment(EQCA) without conditions. A lot of practised theses have been published. They imitate each other and have a great influence. In this paper, however, opposite examples are given and we further prove that using environmental quality fuzzy comprehensive assessment method (EQFCAM) without conditions will lead to the absolutely non-scientific result. Two basic rules that FCA should adhere to are put forward in this paper. They should be the foundation of EQCA. The sufficient and necessary condition that should meet the fundamental rules in methods of FCA will also be given.
