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第30届中国控制与决策会议

作者： Peisuo Yang Yuhan Ding Guohai Liu Congli Mei Xu Chen Hui Jiang School of Electrical and Information Engineering Jiangsu University

To overcome the problems of variable redundancy, long training time and low prediction accuracy in the soft sensing model for marine enzyme fermentation, a gauss process regression（GPR） model based on NN-MIV is presented, which is named as GPR-NNMIV soft sensing model. Firstly, the NN-MIV variable selection method, combining neural network（NN） and mean impact value（MIV）, takes into account both the internal contribution rate and the external contribution rate to get the most suitable input variables with the highest contribution rate, and reduces the number of variables and simplifies soft sensing model. Secondly, based on the NN-MIV method, a new gauss process regression model is proposed, which does not only give out the soft sensing results but also gives the corresponding uncertainty simultaneously. Results show that the proposed GPR-NNMIV soft sensing model has higher accuracy of results and small confidence intervals compared with single gauss process model.

关键词： NN-MIV gauss process regression Marine Enzyme Soft Sensing

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Parameters Identification of Tunnel Jointed Surrounding Rock Based on gaussian process regression Optimized by Difference Evolution Algorithm

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Computer Modeling in Engineering & Sciences 2021年第6期127卷 1177-1199页

作者： Annan Jiang Xinping Guo Shuai Zheng Mengfei Xu Highway and Bridge Institute Dalian Maritime UniversityDalian116026China

Due to the geological body uncertainty,the identification of the surrounding rock parameters in the tunnel construction process is of great significance to the calculation of tunnel *** ubiquitous-joint model and three-dimensional numerical simulation have advantages in the parameter identification of surrounding rock with weak planes,but conventional methods have certain problems,such as a large number of parameters and large time *** solve the problems,this study combines the orthogonal design,gaussian process(GP)regression,and difference evolution(DE)optimization,and it constructs the parameters identification method of the jointed surrounding *** calculation process of parameters identification of a tunnel jointed surrounding rock based on the GP optimized by the DE includes the following ***,a three-dimensional numerical simulation based on the ubiquitous-joint model is conducted according to the orthogonal and uniform design parameters combing schemes,where the model input consists of jointed rock parameters and model output is the information on the surrounding rock displacement and ***,the GP regress model optimized by DE is trained by the data ***,the GP model is integrated into the DE algorithm,and the absolute differences in the displacement and stress between calculated and monitored values are used as the objective function,while the parameters of the jointed surrounding rock are used as variables and *** proposed method is verified by the experiments with a joint rock surface in the Dadongshan tunnel,which is located in Dalian,*** obtained calculation and analysis results are as follows:CR=0.9,F=0.6,NP=100,and the difference strategy DE/Best/1 is *** results of the back analysis are compared with the field monitored values,and the relative error is 4.58%,which is *** algorithm influencing factors are also discussed,and it is found that the local correlation

关键词： gauss process regression differential evolution algorithm ubiquitous-joint model parameter identification orthogonal design

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