In engineering applications, some complicated nonlinear systems are often encountered. The equations of state of these systems are complex and difficult to model accurately by mathematical methods. In this case, BP neural networks can be established to express these nonlinear systems. The unknown system is regarded as a black box, first with the system input and output data to train the BP neural network, the network can express the unknown function, and then use the trained BP neural network to predict the output of the system.

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