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This paper establishes genetic neural network model by combing BP neural network with genetic algorithm and applies the model to predicting bearing capacity of reinforced concrete beams strengthened by carbon fiber sheets, by comparing the predicted values and experimental values, it proves that the model has good ability of forecasting.
The BP neural network algorithm has characteristics of slow convergence speed and local minimum value which could cause the loss of global optimal solution. In order to eliminate the shortcoming of BP neutral network algorithm, genetic algorithm is been put forward to optimize authority value and threshold value of BP nerve network. This paper establishes genetic neural network model. Study has been...
Accurate springback prediction and control is essential for sheet metal forming. In this paper, back propagation (BP) neural network and genetic algorithm (GA) was introduced to predict springback of complex sheet metal forming parts. GA was used to optimize the weights of BP neural network and the results were compared with those of traditional BP neural network and regression model. The comparison...
According to the high missing report rate and high false report rate of existing intrusion detection systems, the paper proposed an anomaly detection model based on genetic neural network, which combined the good global searching ability of genetic algorithm with the accurate local searching feature of BP Networks to optimize the initial weights of neural networks. The practice overcame the shortcomings...
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