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利用BP神经网络高度的非线性函数逼近的能力,解决连续搅拌反应釜的动态液位测量的问题.介绍了BP的基本算法及其改进.并通过实验数据的学习建立了较为有效的动态液位测量网络模型.
The problem of dynamic liquid level measurement in a continuous stirred tank reactor is solved by using the highly nonlinear function approximation ability of BP neural network.The basic BP algorithm and its improvement are introduced.The experimental data is used to establish a more effective dynamic liquid level Measurement network model.