Notice of RetractionFault diagnosis model for automobile engine based on gradient genetic algorithm

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.

In order to solve the fault diagnosis problem of vibration Parameter, this dissertation proposes the application of adaptive neural network-based fuzzy inference system to engine error diagnosis. Different from the fuzzy inference system, the membership function adopted in this method is no longer a fixed entity but an optimal one achieved by the practice of neural network, which adopts the method of information fusion in entropy method to optimize the input interface. By using gradient descent genetic algorithm and optimization of system parameters of neutral network learning algorithm, so as to speed up learning. Based on the adaptive neural network-based fuzzy inference system, experiments show that this system is superior to individual neural network and fuzzy comprehensive evaluation model system in the aspect of stability, recognition rate, and fitting capability.