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Nonlinear inverse modeling of sensor based on back-propagation fuzzy logical system

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Journal of Pharmaceutical Analysis 2007年第1期19卷 14-17页

作者：李军刘君华 School of Automation and Electrical Engineering Lanzhou Jiaotong University School of Electrical Engineering Xi'an Jiaotong University

Objective To correct the nonlinear error of sensor output,a new approach to sensor inverse modeling based on Back-Propagation Fuzzy Logical System(BP FS) is *** The BP FS is a computationally efficient nonlinear universal approximator,which is capable of implementing complex nonlinear mapping from its input pattern space to the output with fast convergence *** The neuro-fuzzy hybrid system,*** FS,is then applied to construct nonlinear inverse model of pressure *** experimental results show that the proposed inverse modeling method automatically compensates the associated nonlinear error in pressure estimation,and thus the performance of pressure sensor is significantly *** The proposed method can be widely used in nonlinearity correction of various kinds of sensors to compensate the effects of nonlinearity and temperature on sensor output.

关键词： sensor inverse modeling fuzzy logical system back-propagation algorithm

Machine Learning-based inverse Model for Few-Mode Fiber Designs

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Computer Systems Science & Engineering 2023年第4期45卷 311-328页

作者： Bhagyalaxmi Behera Gyana Ranjan Patra Shailendra Kumar Varshney Mihir Narayan Mohanty ITER Siksha‘O’Anusandhan(Deemed to be University)Bhubaneswar751030India Department of Electronics and Electrical Communication Engineering IITKharagpur721302India

The medium for next-generation communication is considered as fiber for fast,secure communication and switching *** division and space division multiplexing provide an excellent switching capability with high data transmission *** this work,the authors have approached an inverse modeling technique using regression-based machine learning to design a weakly coupled few-mode fiber for facilitating mode division *** technique is adapted to predict the accurate profile parameters for the proposed few-mode fiber to obtain the maximum number of *** is for a three-ring-core few-mode fiber for guiding five,ten,fifteen,and twenty *** types of regression models namely ordinary least-square linear multi-output regression,k-nearest neighbors of multi-output regression,and ID3 algorithm-based decision trees for multi-output regression are used for predicting the multiple profile *** is observed that the ID3-based decision tree for multioutput regression is the robust,highly-accurate machine learning model for fast modeling of *** proposed fiber claims to be an efficient candidate for the next-generation 5G and 6G backhaul networks using mode division multiplexing.

关键词： Few-mode fibers inverse modeling machine learning regression ring-core

Frame Invariance and Scalability of Neural Operators for Partial Differential Equations

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Communications in Computational Physics 2022年第7期32卷 336-363页

作者： Muhammad I.Zafar Jiequn Han Xu-Hui Zhou Heng Xiao Kevin T.Crofton Department of Aerospace and Ocean Engineering Virginia TechBlacksburgVirginiaUSA Center for ComputationalMathematics Flatiron InstituteNew YorkUSA

Partial differential equations(PDEs)play a dominant role in themathematicalmodeling ofmany complex dynamical *** these PDEs often requires prohibitively high computational costs,especially when multiple evaluations must be made for different parameters or *** training,neural operators can provide PDEs solutions significantly faster than traditional PDE *** this work,invariance properties and computational complexity of two neural operators are examined for transport PDE of a scalar *** operator based on graph kernel network(GKN)operates on graph-structured data to incorporate nonlocal *** we propose a modified formulation of GKN to achieve frame *** cloud neural network(VCNN)is an alternate neural operator with embedded frame invariance which operates on point cloud ***-based neural operator demonstrates slightly better predictive performance compared to ***,GKN requires an excessively high computational cost that increases quadratically with the increasing number of discretized objects as compared to a linear increase for VCNN.

关键词： Neural operators graph neural networks constitutive modeling inverse modeling deep learning

Geophysical and Production Data History Matching Based on Ensemble Smoother with Multiple Data Assimilation

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Computer modeling in Engineering & Sciences 2020年第5期123卷 873-893页

作者： Zelong Wang Xiangui Liu Haifa Tang Zhikai Lv Qunming Liu University of the Chinese Academy of Sciences Beijing100049China Institute of Porous Flow and Fluid Mechanics The Chinese Academy of SciencesLangfang065007China Petro China Research Institute of Petroleum Exploration and Development Beijing100083China

The Ensemble Kalman Filter(EnKF),as the most popular sequential data assimilation algorithm for history matching,has the intrinsic problem of high computational cost and the potential inconsistency of state variables updated at each loop of data assimilation and its corresponding reservoir simulated *** problem forbids the reservoir engineers to make the best use of the 4D seismic data,which provides valuable information about the fluid change inside the ***,only matching the production data in the past is not enough to accurately forecast the future,and the development plan based on the false forecast is very likely to be *** solve this problem,we developed a workflow for geophysical and production data history matching by modifying ensemble smoother with multiple data assimilation(ESMDA).In this work,we derived the mathematical expressions of ESMDA and discussed its scope of *** geophysical data we used is P-wave impedance,which is typically included in a basic seismic interpretation,and it directly reflects the saturation change in the *** resolution of the seismic data is not necessary,we subsampled the P-wave impedance data to further reduce the computational *** our case studies on a benchmark synthetic reservoir model,we also showed the supremacy of matching both geophysical and production data,than the traditional reservoir history matching merely on the production data:the overall percentage error of the observed data is halved,and the variances of the updated forecasts are reduced by two orders of the magnitude.

关键词： History matching data assimilation inverse modeling ESMDA.

Current studies on air distributions in commercial airliner cabins

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Theoretical & Applied Mechanics Letters 2013年第6期3卷 1-5页

作者： Wei Liu Qingyan Chen School of Mechanical Engineering Purdue University School of Environmental Science and Technology Tianjin University

Abstract Air distribution in commercial airliner cabins is very important for the comfort and health of passengers and crew. Experimental measurements, computational fluid dynamics （CFD） simulations, and inverse modeling are state-of-the-art methods available for studying the air distri- bution. This paper gave an overview of the different experimental models, such as scale models, simplified models, full-scale mockups, and actual air cabins. Although experimental measurements were expensive and time consuming, the data were essential for validating CFD simulations. Different modeling strategies for CFD simulations were also discussed in this paper, including large eddy simulations and Reynolds averaged Navier-Stokes equation modeling. CFD simulations were main stream approaches for studying the air distribution but they could not easily lead to optimal design. inverse modeling of air distribution has recently emerged into a very powerful and attractive tool for designing the air distribution in airliner cabins, although most of the studies were preliminary.

关键词： aircraft cabin airflow experiment CFD inverse modeling