Example 1 uses the momentum gradient descent algorithm to train the BP network. Example 2 uses the Bayesian regularization algorithm to improve the generalization ability of BP network. In this example, we use two training methods, the LM optimization algorithm (trainlm) and the Bayesian regularization algorithm (trainbr), to train the BP network to fit a sine sample with white noise data.

bp1.doc
bp神经网络实例.txt
ann.m

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