Feedforward Chaotic Neural Network Model for Rotor Rub-Impact Fault Recognition Using Acoustic Emission Method

Table 1

FCNN model parameter learning.

Step 1:	set the initialization of network weights, the numbers of layers and nodes for each layer, and the initial weight coefficients to ensure the convergence conditions and activation functions
Step 2:	in the forward phase, calculate outputs of the training instance from the signals through the network and calculate the error function between the actual output and the desired output . Besides, update the output weights , neuron B feedback weights , neuron F and input weights , and the neuron threshold
Step 3:	if the derivation is less than the threshold or the iteration has been reached, then the training is over; otherwise, go to the backward phase, building the correcting terms with derivation, output weights, backward weights, and learning rate in order to revise each weight, respectively
Step 4:	repeat forward and backward phases until it satisfies the convergence conditions