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Active noise reduction algorithm parameter optimization method based on particle swarm algorithm

A particle swarm algorithm and active noise reduction technology, applied in computing, computing models, instruments, etc., can solve problems such as low precision and cumbersome operation, and achieve the effect of improving efficiency and precision

Active Publication Date: 2020-09-08
JILIN UNIV
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Problems solved by technology

[0004] However, in experimental research or practical application, for multiple parameters involved in the algorithm, the trial and error method or orthogonal method is generally used to determine the optimal value. These methods are cumbersome to operate and have low precision.

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  • Active noise reduction algorithm parameter optimization method based on particle swarm algorithm
  • Active noise reduction algorithm parameter optimization method based on particle swarm algorithm
  • Active noise reduction algorithm parameter optimization method based on particle swarm algorithm

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Embodiment Construction

[0039] The present invention will be further described in detail below in conjunction with the accompanying drawings, so that those skilled in the art can implement it with reference to the description.

[0040] Such as figure 1As shown, the present invention provides a method for optimizing the parameters of the active noise reduction algorithm based on the particle swarm optimization algorithm. The parameters are set as optimization parameters in the particle swarm optimization algorithm, and the mean square value of the residual error output by the active noise control is set as the fitness function or objective function of the particle swarm optimization algorithm, aiming to find the optimal parameters that minimize the objective function.

[0041] Its steps include:

[0042] Step 1: Random initialization of the population;

[0043] Step 2: Assign the group to the parameter var to be requested i ;

[0044] Step 3: Use the parameters obtained in step 2 to call the adopt...

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Abstract

The invention discloses an active noise reduction algorithm parameter optimization method based on a particle swarm algorithm. The method comprises the steps: step 1, randomly initializing a population; step 2, assigning the group to a parameter to be solved; step 3, operating an active noise control algorithm, and calculating a fitness value corresponding to the fitness function; step 4, determining an individual optimal position and a global optimal position; step 5, judging whether the number of iterations reaches an upper limit or not; if the number of iterations does not reach the upper limit, continuing to execute the following step 6; and if the number of iterations reaches the upper limit, outputting an optimization variable value; and step 6, updating the speed and the position ofthe particle to obtain a new group, and then executing the steps 2 to 5 again.

Description

technical field [0001] The present invention relates to the technical field of active noise control, and in particular to a parameter optimization method of an active noise reduction algorithm based on a particle swarm algorithm. Background technique [0002] With people's pursuit of comfort and NVH performance in the car, more and more attention has been paid to the noise inside the car. Being in a noisy environment for a long time will not only affect driving safety, but also cause great harm to people's physical and mental health. Therefore, controlling interior noise has become an important research direction in the automotive industry. Traditional passive noise control is very effective in controlling high-frequency noise, but not ideal in controlling low-frequency noise. The active noise control uses the principle of sound wave interference, which can suppress low-frequency noise very well, and can effectively improve the sound quality in the car. Among them, the ad...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G10K11/175G06N3/00
CPCG10K11/175G06N3/006Y02T90/00
Inventor 陈书明蒋尧周政道张瑞
Owner JILIN UNIV
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