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Fan noise prediction method based on optimal neural network

A neural network and prediction method technology, applied in the field of noise prediction, can solve the problems of affecting prediction accuracy, long training time, low training accuracy, etc., to improve training accuracy, avoid over-fitting and under-fitting, and reduce construction difficulty Effect

Active Publication Date: 2018-08-21
ZHUZHOU LINCE GRP
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Problems solved by technology

For the number of input neurons that has been determined, if the number of neurons in the hidden layer is small, the training accuracy will be low, and underfitting will occur. If the number of neurons in the hidden layer is large, the training accuracy will be high, the training time will be long, and overfitting will occur, which will affect the prediction accuracy.

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  • Fan noise prediction method based on optimal neural network
  • Fan noise prediction method based on optimal neural network
  • Fan noise prediction method based on optimal neural network

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Embodiment Construction

[0068] The embodiments of the present invention will be described in detail below with reference to the accompanying drawings, but the present invention can be implemented in various ways defined and covered by the claims.

[0069] Such as figure 1 A fan noise prediction method with optimized neural network is shown, including steps:

[0070] S1. Collect fan performance parameters and geometric parameters;

[0071] There are 6 performance parameters, including: 1-flow, 2-total pressure, 3-speed, 4-power, 5-efficiency, 6-exit wind speed; there are 10 geometric parameters, including: 7-impeller diameter, 8-hub Ratio, 9-blade root installation angle, 10-blade root chord length, 11-blade middle installation angle, 12-blade middle chord length, 13-blade tip installation angle, 14-blade tip chord length, 15-blade number, 16 - Number of guide vanes.

[0072] S2. Analyze the status quo of fan performance parameters;

[0073] Perform performance analysis on the fan parameters teste...

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Abstract

The invention provides a fan noise prediction method based on an optimal neural network. The fan noise prediction precision and the generalization ability of the neural network are improved mainly through joint control on the number of input neurons and the number of hidden layer neurons. Based on correlation analysis, the influencing importance of input parameters in fan samples towards output parameters is sorted, and according to the training precision and the prediction precision, an input layer neuron number range and the maximum input layer neuron number are determined. The correlation analysis is used to effectively reduce the input neuron number, and the building difficulty of the optimal neural network structure is reduced. The best hidden layer neuron number is used to determinethe optimal neural network structure, over fitting and under fitting can be effectively avoided, and while the training precision is improved, the prediction precision and the generalization ability are also improved.

Description

technical field [0001] The invention relates to the field of noise prediction, in particular to a fan noise prediction method with optimized neural network. Background technique [0002] There are many types of fans and a wide range of applications. Different types of fans, fans in different applications, and fans with different functions have different requirements for allowable noise, which can range from more than ten decibels to nearly one hundred decibels. There are many sources of fan noise, such as: blade rotation, blade vortex, turbulent flow, and resonance with the duct shell. These are all fixed noises of the fan. In addition, there are abnormal noises caused by external assembly and maintenance. Usually there are certain standards for the tolerance of fan noise in specific occasions, so it is particularly important to accurately predict fan noise. [0003] Neural network is a mathematical model that uses a structure similar to that of brain synaptic connections ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06N3/02
CPCG06N3/02G06Q10/04
Inventor 刘梦安杨奇阳吉初翟方志侯志泉屈小章
Owner ZHUZHOU LINCE GRP
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