Title :
Notice of Retraction
Combinatorial Predict Model of Enterprise Profit Based on Stochastic Partial Elasticity Theory and Artificial Neural Network
Author :
Chunzhao Liu ; Dan Ma ; Haojin Lv
Author_Institution :
Dept. of Marketing, Ningbo Polytech., Ningbo, China
Abstract :
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
Searching for a reliable enterprise profit predict model is the key point to ensure the enterprise to get a scientific management. After getting the two predict sequences by stochastic partial elasticity theory and artificial neural network, a nonlinear model is established to solve the combinatorial weighted coefficients by advance the predict accuracy to get a new predict value. Numerical simulation results show that the new predict model of enterprise profit has strong generalization ability and can improve the predict accuracy effectively.
Keywords :
combinatorial mathematics; elasticity; neural nets; profitability; stochastic processes; artificial neural network; combinatorial predict model; combinatorial weighted coefficients; generalization ability; nonlinear model; numerical simulation; predict sequences; reliable enterprise profit predict model; scientific management; stochastic partial elasticity theory; Accuracy; Artificial neural networks; Biological system modeling; Elasticity; Mathematical model; Predictive models; Stochastic processes; artificial neural network; predict accuracy; stochastic partial elasticity theory; weighted coefficient;
Conference_Titel :
Modeling, Simulation and Visualization Methods (WMSVM), 2010 Second International Conference on
Conference_Location :
Sanya
Print_ISBN :
978-1-4244-7077-8
DOI :
10.1109/WMSVM.2010.74
