摘要
In this paper, a method of optimizing the number of hidden layer neurons has been put forward. This optimizing method is suitable for three layers B-p network. The purpose of this optimizing method is to reduce the predicting errors when the model is used as predicting model. As an example of application, a predicting model of steel end-quench curves has been designed by using this optimizing method. The result shows that the optimization of ANN hidden layer architecture has an effect on reducing predicting errors.
In this paper, a method of optimizing the number of hidden layer neurons has been put forward. This optimizing method is suitable for three layers B-p network. The purpose of this optimizing method is to reduce the predicting errors when the model is used as predicting model. As an example of application, a predicting model of steel end-quench curves has been designed by using this optimizing method. The result shows that the optimization of ANN hidden layer architecture has an effect on reducing predicting errors.
