Transformer noise suppression method

Patent metadata

Field	Value
Publication number	US-10083709-B2
Application number	US-201515323407-A
Country	US
Kind code	B2
Filing date	Jan 15, 2015
Priority date	May 6, 2014
Publication date	Sep 25, 2018
Grant date	Sep 25, 2018

Abstract

The noise suppression method of individual active noise reduction system comprises the steps that: (1) initial noise digital signals are received and converted to serve as input signals of a BP neural network; (2) the input signals are processed to generate secondary digital signals; (3) the secondary digital signals are output to a loudspeaker and secondary noise is generated; (4) remained noise digital signals obtained by overlapping the initial noise and the secondary noise are received; whether remained noise digital signals is continuously constant for the set times is judged; if yes, the secondary digital signals are kept outputting; (5) if not, BP neural network parameters are optimized and adjusted with the amplitude of the remained noise digital signals being minimum as the optimality principle; remained noise digital signals of previous step are served as new input signals and the step (2) is executed again.

First claim

The invention claimed is:

1. 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 ω hi (n) between the input layer neuron i and the hidden layer neuron h in the neural network, the weight coefficient W h (n) between the hidden layer neuron h and the output layer, the threshold value GE h (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:

ω 11
ω 12
. . .
ω K
W 1
GE 1
. . .
ω J1
ω J2
. . .
ω JK
W J
GE J
ge.

2. The transformer noise suppression method according to claim 1 is characterized by the following steps:
The step of optimizing and adjusting the parameters of the BP neural network in the fifth step is carried out by the improved particle swarm optimization algorithm which further comprising:
Step 1: determining, dimension of particles, according to the structure of the BP neural network and generating N initial particles randomly;
Step 3: Selecting the residual noise signal e(n) of the system as a judgment criterion of network parameters, and using the following fitness function F(n) as the particle position coding and formula:

F
⁡

(
n
)

=

(

x
⁡

(
n
)

⋆

h
1

⁡

(
n
)

-

y
⁡

(
n
)

⋆

h
2

⁡

(
n
)

)

2

2

[
1
]

in formula [1] x(n) is the initial noise digital signal input at the nth time; H 1 (z) and H 2 (z) are the transfer functions of the primary channel and the secondary channel respectively; y(n) is the digital signal of the network output at the n-th time, and the formula is:

y
⁡

(
n
)

=

∑

h
=
1

J

⁢

Assignees

• State Grid Jiangsu Electric Power Co Nanjing Power Supply Co
• State Grid Jiangsu Electric Power Co
• State Grid Company Corp Of China
• Univ Hohai
• State Grid Jiangsu Electric Power Company Nan Jing Power Supply Company
• State Grid Company Corporation Of China Hohai Univ

Inventors

• Jiang Ning
• Ma Hongzhong
• Li Kai
• Xu Honghua
• Wang Chunning
• Chen Bingbing
• Jiang Hongyu

Classifications

• G06N7/01

  Probabilistic graphical models, e.g. probabilistic networks · CPC title

• G06N3/08

  Learning methods · CPC title

• H04R3/005

  for combining the signals of two or more microphones (specially adapted for hearing aids H04R25/407) · CPC title

• G06N3/04

  Architecture, e.g. interconnection topology · CPC title

• H01F27/33

  Arrangements for noise damping · CPC title