Method of constructing response surface model on the basis of genetic algorithm and system applying same

Abstract

The invention relates to the field of complex product design and manufacture, and particularly to a method of constructing a response surface model on the basis of a genetic algorithm and a system applying the same. The method comprises the following steps: a, carrying out sampling, wherein desired parameters or standards are input, and sampling data are obtained by a method of Latin hypercube sampling; b, constructing a basis function dictionary, wherein the mixed dictionary is constructed; c, seeking an algorithm of sparse representation, wherein after the mixed dictionary in the step b is constructed, coefficients corresponding to all basis functions in the mixed dictionary are obtained by solving by the simulated genetic algorithm according to X and Y in the sampling data in the step a; d, establishing the model; e, obtaining an original simulation model value yt corresponding to an independent variable xt in complex product design; and f, outputting machining parameters to use the same on production and manufacture of a complex mechanical and electrical product. According to the method, simulated genetic thinking is utilized to seek expression, which is low in sparseness and is high enough in precision at the same time, on the mixed dictionary, and thus the more precise and concise response surface model is constructed.

Description

technical field [0001] The invention relates to the field of product design and manufacture, in particular to a method and a system for constructing a complex electromechanical product response surface model. Background technique [0002] Facing fierce competition in the mechanical and electrical product market, manufacturing companies must continuously improve their design efficiency while ensuring product quality to meet the diverse market needs. For the design and manufacture of complex electromechanical products (such as the shape and structure of aircraft, automobiles and ships, etc.), it is essentially an optimization process involving multi-disciplinary, multi-objective and "expensive" simulation. The complexity of the analytical calculation model is also increasing, such as finite element analysis (Finite Element Analysis, FEA), computational fluid dynamics analysis (Computational FluidDynamics, CFD) and multi-body dynamics simulation (Multi-body Dynamics Simulation,...

AI Technical Summary

Problems solved by technology

However, existing algorithms such as MP, OMP, and BP can only achieve a better sparsity under the condition of ensuring the accuracy of the reconstructed response surface model, and cannot be applied to the expression of small sparsity and high enough progress at all. Unable to build a more accurate and concise response surface model

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