A new method to improve prediction precision of GM（1,1） model with unequal time interval is presented.The grey derivative is multiplied by a parameter to guarantee the time response function satisfying approximately...A new method to improve prediction precision of GM（1,1） model with unequal time interval is presented.The grey derivative is multiplied by a parameter to guarantee the time response function satisfying approximately exponential function distribution.To simplify the process of parametric estimation,an approximate value is taken for the multiplied parameter.Then the estimators of coefficient of development and grey action quantity can be derived.At the same time,the principle of the new information priority is also considered.We take the last item of the first-order accumulated generation operator（1-AGO） on raw data sequence as the initial condition in the time response function.Then the new information can be taken full advantage of through the improved initial condition.Some properties of this new model are also discussed.The presented method is actually a combination of improvement of grey derivative and improvement of the initial condition.The results of an example indicate that the proposed method can improve prediction precision prominently.展开更多
Based on both white response and connotation expression are geometric progression in the most primitive grey differential equation of GM（1,1）x（k） （k）＋ ax（1） （k） = b, this paper begins with generation of the...Based on both white response and connotation expression are geometric progression in the most primitive grey differential equation of GM（1,1）x（k） （k）＋ ax（1） （k） = b, this paper begins with generation of the time response .function＇s grey derivative at discrete points. Through derivative＇s definition, establishing a new GM（1,1） by optimizing grey derivative and background value. Then, getting the best coefficient c by introducing criterion function and it has proved that the new expression has the whitened exponent law coincident property and the whitened coefficient coincident property in theory. Finally, some examples show the new model has higher prediction precision.展开更多
基金supported by the National Natural Science Foundation of China (7090103471071077)+2 种基金the National Educational Sciences Planning Key Project of Ministry of Education (DFA090215)the Fundamental Research Funds for the Central Universities (JUSRP21146JUSRP31107)
文摘A new method to improve prediction precision of GM（1,1） model with unequal time interval is presented.The grey derivative is multiplied by a parameter to guarantee the time response function satisfying approximately exponential function distribution.To simplify the process of parametric estimation,an approximate value is taken for the multiplied parameter.Then the estimators of coefficient of development and grey action quantity can be derived.At the same time,the principle of the new information priority is also considered.We take the last item of the first-order accumulated generation operator（1-AGO） on raw data sequence as the initial condition in the time response function.Then the new information can be taken full advantage of through the improved initial condition.Some properties of this new model are also discussed.The presented method is actually a combination of improvement of grey derivative and improvement of the initial condition.The results of an example indicate that the proposed method can improve prediction precision prominently.
文摘Based on both white response and connotation expression are geometric progression in the most primitive grey differential equation of GM（1,1）x（k） （k）＋ ax（1） （k） = b, this paper begins with generation of the time response .function＇s grey derivative at discrete points. Through derivative＇s definition, establishing a new GM（1,1） by optimizing grey derivative and background value. Then, getting the best coefficient c by introducing criterion function and it has proved that the new expression has the whitened exponent law coincident property and the whitened coefficient coincident property in theory. Finally, some examples show the new model has higher prediction precision.
