Notice of RetractionThree-phase SPWM inverter fault diagnosis based on optimized neural networks

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.

The problem that how to use hierarchical genetic algorithm to determine the structure and parameters of neural networks is studied. The two grade coding structure of the hierarchical genetic algorithm is utilized to solve the ancient problem that when optimize the neural networks´ structure, connection weights, threshold at the same time, the efficiency was low. Furthermore, we compare the networks´ capability that optimized by the improved hierarchical genetic algorithm with the ones optimized by other algorithm, and prove the algorithm´s credibility through the simulation. At last, the improved adaptive genetic algorithm is used in the fault diagnosis of three-phase SPWM inverter circuit, and the simulation result show the method is correct and applied.