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A Noise Prediction Method of Internal Combustion Engine Based on vmd and narx

A prediction method and technology of internal combustion engine, applied in prediction, neural learning method, data processing application, etc., can solve the problems of long operation time and high time cost, improve accuracy and timeliness, good applicability, and simplify and stabilize the processing process Effect

Active Publication Date: 2022-04-05
HARBIN ENG UNIV
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Problems solved by technology

At the same time, during the training process of the neural network, the calculation time is generally longer, and the time cost is higher.

Method used

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  • A Noise Prediction Method of Internal Combustion Engine Based on vmd and narx
  • A Noise Prediction Method of Internal Combustion Engine Based on vmd and narx
  • A Noise Prediction Method of Internal Combustion Engine Based on vmd and narx

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Embodiment 1

[0051] Step 1: Obtain the time series data of internal combustion engine noise, construct a training set D={X,Y}, X={x 1 ,x 2 ,...,x n}, Y={y 1 ,y 2 ,...,y n}; x i for t i The value of the noise signal of the internal combustion engine acquired at any time, y i for t i The value of the noise signal of the internal combustion engine obtained at time +T; i={1,2,...,n}, T is the predicted time difference;

[0052] Step 2: Use VMD to analyze the internal combustion engine noise signal time series X={x 1 ,x 2 ,...,x n} to be processed and decomposed to obtain K groups of modal components {U 1 ,U 2 ,...,U K}, U k ={u 1k ,u 2k ,...,u nk},k={1,2,...,K};

[0053] Step 2.1: The VMD process needs to decompose the input time series into a variational framework, and achieve adaptive signal decomposition by finding the optimal solution of the constrained variational model.

[0054] By solving the variational iterative model, the frequency band of the adaptively decomposed...

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Abstract

The invention belongs to the technical field of noise prediction of internal combustion engines, and in particular relates to a noise prediction method of internal combustion engines based on VMD and NARX. In the process of processing the noise signal of the internal combustion engine, the present invention uses the variational mode decomposition technology to realize signal separation, obtain signals with different frequency characteristics, systematically analyze the characteristics of the noise signal of the internal combustion engine, and perform various modal component signals through the NARX neural network. Prediction acquires signal characteristics at moments that do not occur. The invention simplifies the smoothing process of the noise signal of the internal combustion engine, effectively improves the accuracy and timeliness; combines the optimization algorithm, improves the prediction efficiency, has better applicability, and can more accurately predict the noise value of the internal combustion engine at the next moment.

Description

technical field [0001] The invention belongs to the technical field of internal combustion engine noise prediction, and in particular relates to a method for predicting internal combustion engine noise based on VMD and NARX. Background technique [0002] Time series prediction is of great significance in engineering practice. Through the accurate prediction of time series and the management of its trend, corresponding measures can be formulated in a targeted manner, and the characteristics of time series can be grasped, so as to deal with the uncertainty of future situations. [0003] Traditional time forecasting methods are mainly based on mathematics and statistics, through linear regression or least squares regression analysis on time series, to establish the connection between forecast data and historical data. As a classic time series forecasting model, the autoregressive moving average (ARMA) model has rigorous theoretical support. The ARMA model can be regarded as a ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06Q10/04G06N3/04G06N3/08
CPCG06Q10/04G06N3/08G06N3/045
Inventor 沈艳王萍王雪松孙科黄瑾张志新徐晓迪朱新林宋星宇
Owner HARBIN ENG UNIV
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