Notice of RetractionResearch on identification of coal and waste rock based on PCA and GA-ANN

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.

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When exploring identification of coal and waste rock, 17 characteristic parameters of gray-scale histogram and gray level co-occurrence matrix (GLCM) were chosen according to their differences in gray scale and texture. Then, the principal component analysis (PCA) algorithm was used to get principal components from all the parameters chosen above. The principal components were defined as the inputs of GA-ANN to identify the experimental samples of coal and waste rock. The identification rate reaches up to 100% through simulating experiments which proves the feasibility of the characteristic parameters and principal component analysis. Finally, by comparing the simulation results with BP neural network, the identification not only demonstrates the validity in extracting the principal components from the 17 parameters, but also shows that the GA-ANN algorithm is superior to the traditional BP neural network in pattern recognition. The application of PCA combined with GA-ANN provides a new method in intelligent identification for coal and waste rock.