A Method of Building a Surrogate Model for Complex Product Optimal Design Based on Small Samples

Abstract

The invention relates to a method for establishing a complex product optimization design agent model based on a small sample, belonging to the technical field of complex product optimization design. The method comprises the following steps: setting a goal of the complex product optimization design; generating an original design scheme sample set, the sample capacity of which is S, for the complex product optimization design; generating a virtual design scheme sample set for the complex product optimization design; combining the original design scheme sample set with the virtual design scheme sample set so as to form a mixed design scheme sample set; determining the sensitivities of the target of the complex product optimization design corresponding to various decision variables, and sorting the sensitivities; establishing a three-layer BP neural network model having different input variables by taking the target variable as the output variable; and training various neural network models by taking the mixed sample set as the training sample set; and selecting the neural network model having the optimal performance as the final complex product optimization design agent model. By means of the method, the sample generation workload is reduced; and the precision of the complex product optimization design agent model is ensured.

Description

technical field [0001] The invention belongs to the technical field of complex product optimization design, in particular to a method for establishing a complex product optimization design agency model based on small samples. Background technique [0002] Complex products refer to a class of products with complex customer needs, complex product composition, complex product technology, complex manufacturing process, and complex production management. Aircraft, engines, ships, and machine tools are typical representatives of such products. Complex product optimization design is an optimization process that continuously adjusts design parameters to form a new solution, and evaluates whether the adjustment is effectively approaching the design goal. This is a process of trial and error. If the original design method is used for the effectiveness evaluation of each tentative adjustment, it will bring unacceptable amount of calculation, which will eventually lead to the infeasibil...

AI Technical Summary

Problems solved by technology

However, the technical complexity shown in the complex product design process leads to the high cost of generating design samples, and it is difficult to obtain enough training samples, which leads to the inability to estimate some feature extraction parameters and the inability to establish an accurate proxy model; in addition, how to fully Considering the high-dimensional problems brought about by technical complexity, choosing a cost-effective model will fundamentally determine the overall quality of the optimized design

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