Virtual sample generation method

Abstract

The present invention discloses a virtual sample generation method comprising the following steps: firstly, acquiring a limited quantity of high dimensional real samples by adopting means of signal acquisition and corresponding devices, then constructing a feasibility based planning (FBP) model by adopting a partial least square (PLS) algorithm, a genetic (GA) algorithm and a back propagation neural network (BPNN) algorithm; secondly, generating an input of a virtual sample on the basis of future knowledge of the known real sample; thirdly, inputting PLS extracted potential features of the virtual sample into FBP, and acquiring an output of the virtual sample on the basis of the future knowledge; and finally, combining input vectors and output vectors of the virtual sample according with a preset rule to acquire a complete virtual sample. Therefore, the virtual sample that can be used for predicting high dimensional data is generated relatively accurately.

Description

technical field

[0001] The invention relates to the technical field of machine learning, in particular to a virtual sample generation method. Background technique

[0002] Machine learning techniques based on big data have been widely and successfully applied in many different industries. For the medical records of many rare diseases and the early stages of flexible manufacturing systems, only a small number of training samples can be used to construct predictive models. For complex process industrial processes, in order to achieve its optimal control and operation optimization, it is necessary to measure or predict the difficult-to-detect process parameters of key mechanical equipment, such as the internal load parameters of the grinding machine, which are difficult to directly detect and directly calculated by using a mechanism model , mainly using the soft sensing method based on the high-dimensional spectrum data of vibration and vibro-acoustic signals of the mill cylin...

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