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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.
To avoid some defects of the tradition BP neural network, a new combination algorithm with the adaptive genetic algorithm and BP neural network (AGA-BP) is used in grinding burn fault diagnosis. In the former stage of the combination algorithm, the adaptive genetic algorithm with self study speed is used to optimize initial weights and thresholds of the BP neural network by learning from the samples in a global view, and in the later stage, the error back propagation algorithm is used to improve local convergence speed of the combination algorithm. Then a program written by visual basic 6.0 is used in the grinding burn fault diagnosis according to the AGA-BP algorithm and generally BP algorithm. The results show that the fault diagnosis precision of AGA-BP algorithm is more correctly than that of the generally BP algorithm.