Two-way Neural Network Computational Inverse Theory Method And Application In Parameter Inverse

The computational inverse techniques can be used to inversely determine the internal characteristics or external environmental parameters of complex structures which are difficult to be determine.It provides an effective solution for many engineering problems.However,engineering problems are very complicated and the calculation scale is large,many inverse problems are ill-posed.The ill-conditioned system kernel matrix and the noise in the measurement response may severely affect the accuracy and stability of the inverse results.Since the outstanding nonlinear mapping ability and global convergence of artificial neural networks,Professor Liu Guirong proposed a two-way trumpet neural network direct weight inversion theory based on the physical model and simulation model.The inverse problem neural network with parameter identification capability can be directly derived by the fully trained forward problem neural network,this greatly improves the efficiency of computational inverse.However,this method has only been derived in theory.When generalized inverse processing is performed on the irreversible weight matrix of the forward problem neural network,regularization parameters should be introduced to improve its noise immunity and solution stability.If the regularization parameters are not selected properly,the solution of inverse problem will deviate from the real value.This paper systematically explores the two-way neural network inverse method,and attempts to overcome the ill-conditioned of the inverse system and functions.Specifically,the research contents of this article are as follows.(1)A two-way tube neural network inverse method is proposed.In this method,the number of neurons in each layer of the forward or inverse problem neural network is equal to the number of parameters to be obtained.This method takes advantage of reversibility of square matrices,effectively spares the introduction of regularization parameters,and overcomes the ill-condition of inverse systems and functions.(2)A method of training an inverse problem neural network using a full trained forward problem neural network as a proxy model is proposed.The trained forward problem neural network is used as the forward problem solver to generate training data for the inverse problem neural network.More the samples required to the inverse problem neural network than the forward problem neural network is,the higher the computational efficiency of this method.This method improves the calculation efficiency while keeping up the completeness of forward problem output.(3)Inverse calculation of composite material parameters is carried out based on the twoway tube neural network direct weight inverse method.This method is based on quasi-static numerical simulation experiments of composite laminates and structural response to perform inverse calculation of material parameters.Compared with other methods in(2),this method has higher accuracy and the solution efficiency increased by more than 40%.(4)Methods that can improve the sensitivity of parameters are explored by adjusting the boundary conditions and loading strategy of the structure.Especially,the sensitivity of insensitive parameters is improved significantly.