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提出了一种高效的BP神经网络学习算法,首次对神经网络输出结果进行自适应残差补偿,基于此提出炼铜转炉吹炼终点组合预报模型.经实践检验.本预报模型的预测结果具有较高的精度和较强的实时性,而且本模型具有较好的自学习能力,能有效指导生产实践。
An efficient BP neural network learning algorithm is proposed. For the first time, adaptive residual compensation is performed on the output of the neural network. Based on this, a combined prediction model for the end point of blowing converter is proposed. Practice test. The prediction results of this forecasting model have higher precision and stronger real-time performance. Moreover, this model has better self-learning ability and can effectively guide the production practice.