Method for predicting life of small sample data object based on dynamic bipolar modified probabilistic neural network (MPNN)

Abstract

The invention discloses a method for predicting the life of a small sample data object based on a dynamic bipolar modified probabilistic neural network (MPNN) and belongs to the technical field of the life prediction of small samples. The method comprises the following steps of: obtaining a function relation between life influencing factors and life characteristic parameters of a prediction object by collecting all available data of a life prediction object and preprocessing life prediction related data and analyzing data dependency for the life prediction object; then mapping data and obtaining equivalent data values of the prediction object; performing a primary MPNN training and prediction and a secondary MPNN training and prediction by using the improved MPNN; and finally, determining life values of the prediction object according to a life termination criterion of the life characteristic parameters of the prediction object. The method can realize the life prediction of the small sample data object by fully using the life influencing factor data of the prediction object under an adverse condition of a little practical available data of the prediction object and has a strong universality; and moreover, a life prediction model can dynamically regulate a neural network and can constantly improve the precision of the life prediction.

Description

technical field [0001] The invention belongs to the technical field of small-sample life prediction, and specifically refers to a life prediction method applied to small-sample data objects. Background technique [0002] Life prediction technology involves a wide range of fields and fields, from the fatigue life of raw materials to the life of complex molded products, from the civil field to the national defense field. At present, there are mainly three methods for carrying out life prediction work: [0003] a, Life prediction based on physical model: This method is based on the analysis of the research object, so as to establish a physical model reflecting the evolution process of the object, adjust the model parameters through relevant data, and finally obtain the required life prediction model. This method is mainly used in the field of materials science; [0004] b. Lifespan prediction based on statistical model assumptions: This method first assumes that the lifespan ...

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Problems solved by technology

However, for the prediction problem, the PNN network is helpless, so it is even more difficult to

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