Method of constructing response surface model based on simulated annealing algorithm and system using method

A technology of simulated annealing algorithm and response surface model, which is applied in special data processing applications, calculations, complex mathematical operations, etc., and can solve problems such as inability to build

Inactive Publication Date: 2017-12-15
GUANGDONG UNIV OF TECH
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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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  • Method of constructing response surface model based on simulated annealing algorithm and system using method
  • Method of constructing response surface model based on simulated annealing algorithm and system using method
  • Method of constructing response surface model based on simulated annealing algorithm and system using method

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Embodiment Construction

[0060] The technical solutions of the present invention will be further described below in conjunction with the drawings and specific implementations.

[0061] The general response surface model in the prior art is formed by linear combination of basis functions and their corresponding coefficients, and has the following expression:

[0062]

[0063] c i Is the coefficient corresponding to the basis function, ψ i (x) is the basis function.

[0064] The first step in constructing the response surface model is to sample in the design space to obtain the sampling point set X({x i |i=1,2,...,m}), and then perform simulation analysis to obtain the response value data set y({yi | i=1, 2,..., m}), and finally X and y construct different approximate models according to different algorithm principles.

[0065] The basis function dictionary is constructed in a typical fixed orthogonal basis dictionary with discrete cosine transform (DiscreteCosine Transform, DCT) basis, discrete Fourier transf...

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Abstract

The invention relates to the field of complex product design and manufacture, in particular to a method for constructing a response surface model based on the simulated annealing algorithm and a system using the method. The method comprises the steps that a, sampling: expected parameters or criteria are inputted, sample data is obtained by the Latin hypercube sampling method; b, construction of a base function dictionary: a mixed dictionary is constructed; c, searching for a sparse representation algorithm: after the mixed dictionary described in step b is constructed, the coefficients of each base function in the mixed dictionary are solved using the simulated annealing algorithm according to the X and Y in the sampled data described in step a; d, a model is established; e, the value yt of the original simulation model corresponding to the independent variable xt in a complex product design is obtained; f, the output processing parameters are used for the manufacture of complex electromechanical products. According to the method for constructing the response surface model based on the simulated annealing algorithm, the simulated annealing idea is used for seeking the expression of small sparsity and high accuracy in the mixed dictionary, and then the more precise and concise response surface model is constructed.

Description

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

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/50G06F17/15
CPCG06F17/15G06F30/367G06F2119/18
Inventor 王科峰黄运保李璞
Owner GUANGDONG UNIV OF TECH
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