Transformer noise suppression method

A method of suppressing noise of a transformer, characterized by the following steps:

• using a system is consisting essentially of a controller including
• an intelligent chip, an initial noise measuring microphone, a residual noise measuring microphone, and a loudspeaker, wherein the controller is configured to suppress noise suppression concentrated in integer multiple frequency of fundamental frequency (100 Hz), in the following steps:
• receiving, in a first step, an initial noise digital signal transmitted and converted by the initial noise measuring microphone in the vicinity of a noise source as an input signal of a BP (Back Propagation) neural network;
• processing, in a second step, the input signal by BP neural network to generate a secondary digital signal whose phase is deviated from the input signal;
• converting, in a third step, the secondary digital signal into an analog signal, amplifying and outputting the secondary signal to the loudspeaker to generate a secondary noise having an inhibitory effect on the initial noise;
• receiving, in a fourth step, the initial noise picked up by the residual noise measurement microphone and the residual noise digital signal superimposed and converted by the secondary noise to determine whether the amplitude of the residual noise digital signal has been continuously set the number of times unchanged, if so, the secondary digital signal output is kept, otherwise proceed to the next step;
• and
• minimizing, in a fifth step, the residual noise digital signal amplitude as the principle of optimization to optimize and adjust the BP neural network parameters, the initial noise digital signal of the next time as the new BP neural network input signal, and then repeat the second step,
• wherein the first-fifth steps are performed sequentially in that order;
• performing the fifth step of optimizing and adjusting the parameters of the BP neural network by an improved particle swarm optimization algorithm, comprising a step of:
• Step 2:
• forming weight coefficient ωhi (n) between the input layer neuron i and the hidden layer neuron h in the neural network, the weight coefficient Wh(n) between the hidden layer neuron h and the output layer, the threshold value GEh(n) of hidden layer neuron h and the threshold value ge (n) of output layer neuron at the nth time are real-number encoded according to the predetermined order, and the corresponding real number particles wherein each particle corresponds to a group of network parameters and an encoding format is:
• ω11
• ω12
• . . .
• ωK
• W1
• GE1
• . . .
• ωJ1
• ωJ2
• . . .
• ωJK
• WJ
• GEJ
• ge.