CNN is essentially an input-to-outp

CNN is essentially an input-to-output mapping. It can learn a large number of input-output mapping relationships without the need for any precise mathematical expressions between input and output, as long as it uses known pattern pairs. The convolutional network is trained, and the network has the ability to map between input and output pairs. The convolutional network performs instructor training, so its sample set is composed of vector pairs of the form (input vector, ideal output vector). All these vector pairs should be derived from the actual "running" results of the system to be simulated by the network. They can be collected from the actual operating system. Before starting training, all weights should be initialized with different small random numbers. "Small random number" is used to ensure that the network will not enter the saturation state due to excessive weights and cause training failure; "different" is used to ensure that the network can learn normally.