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Journal articles on the topic 'Inverse linear model'

Author: Grafiati

Published: 25 May 2024

Last updated: 31 July 2025

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1

BERNARD, James, and Mark PICKELMANN. "An Inverse Linear Model of a Vehicle." Vehicle System Dynamics 15, no. 4 (1986): 179–86. http://dx.doi.org/10.1080/00423118608968850.

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2

Zhanatauov, S. U. "INVERSE MODEL OF MULTIPLE LINEAR REGRESSION ANALYSIS." Theoretical & Applied Science 60, no. 04 (2018): 201–12. http://dx.doi.org/10.15863/tas.2018.04.60.38.

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Borneman, Joshua, Kuo-Ping Chen, Alex Kildishev, and Vladimir Shalaev. "Simplified model for periodic nanoantennae: linear model and inverse design." Optics Express 17, no. 14 (2009): 11607. http://dx.doi.org/10.1364/oe.17.011607.

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4

Ayala, A., M. Loewe, and R. Zamora. "Inverse magnetic catalysis in the linear sigma model." Journal of Physics: Conference Series 720 (May 2016): 012026. http://dx.doi.org/10.1088/1742-6596/720/1/012026.

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5

Fang, Ximing. "A hybrid regularization model for linear inverse problems." Filomat 36, no. 8 (2022): 2739–48. http://dx.doi.org/10.2298/fil2208739f.

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For the ill-posed linear inverse problem, we propose a hybrid regularization model, which possesses the characters of Tikhonov regularization and TV regularization to some extent. Through transformation, the hybrid regularization is reformulated as an equivalent minimization problem. To solve the minimization problem, we present two modified iterative shrinkage-thresholding algorithms (MISTA) based on the fast iterative shrinkage-thresholding algorithm (FISTA) and the iterative shrinkagethresholding algorithm (ISTA). The numerical experiments are performed to show the effectiveness and superio

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Hansen, Thomas Mejer, Andre G. Journel, Albert Tarantola, and Klaus Mosegaard. "Linear inverse Gaussian theory and geostatistics." GEOPHYSICS 71, no. 6 (2006): R101—R111. http://dx.doi.org/10.1190/1.2345195.

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Inverse problems in geophysics require the introduction of complex a priori information and are solved using computationally expensive Monte Carlo techniques (where large portions of the model space are explored). The geostatistical method allows for fast integration of complex a priori information in the form of covariance functions and training images. We combine geostatistical methods and inverse problem theory to generate realizations of the posterior probability density function of any Gaussian linear inverse problem, honoring a priori information in the form of a covariance function desc

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Cho, Jeong-Mok, Bong-Soo Yoo, and Joong-Seon Joh. "A Fuzzy Skyhook Algorithm Using Piecewise Linear Inverse Model." International Journal of Fuzzy Logic and Intelligent Systems 6, no. 3 (2006): 190–96. http://dx.doi.org/10.5391/ijfis.2006.6.3.190.

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Zhou, Huilin, Tao Ouyang, Yadan Li, Jian Liu, and Qiegen Liu. "Linear-Model-Inspired Neural Network for Electromagnetic Inverse Scattering." IEEE Antennas and Wireless Propagation Letters 19, no. 9 (2020): 1536–40. http://dx.doi.org/10.1109/lawp.2020.3008720.

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9

Penland, Cécile, and Ludmila Matrosova. "Expected and Actual Errors of Linear Inverse Model Forecasts." Monthly Weather Review 129, no. 7 (2001): 1740–45. http://dx.doi.org/10.1175/1520-0493(2001)129<1740:eaaeol>2.0.co;2.

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10

Jiang, Wen, Yi Xin Su, and Dan Hong Zhang. "Research on Inverse Control of Active Magnetic Bearing Based on Fuzzy Inverse Model." Applied Mechanics and Materials 575 (June 2014): 744–48. http://dx.doi.org/10.4028/www.scientific.net/amm.575.744.

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For magnetic bearing system with characteristics of zero damping, negative stiffness and nonlinearity, this paper put forward a method of inverse control based on the fuzzy inverse model. The fuzzy system with fuzzifier and defuzzifier was used as an interpolator to approximate the inverse model of magnetic bearing. Then we connected the fuzzy inverse model in series with the magnetic bearing system to form a generalized pseudo linear plant, and selected a PID controller to control the pseudo linear plant. The fuzzy inverse model and the PID controller together formed an inverse controller to

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11

Yip, K. M., and G. Leng. "Stability analysis for inverse simulation of aircraft." Aeronautical Journal 102, no. 1016 (1998): 345–51. http://dx.doi.org/10.1017/s0001924000027597.

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AbstractThe integration inverse method has been extensively investigated in the past decade; however, none of the researches fully addresses the stability analysis of the method that is crucial to successful implementation. This paper presents a stability test to analyse the global stability of the integration inverse method for linear time-invariant systems. A stable solution may be obtained from careful selection of the discretisation interval using the proposed stability test. A discrete model is derived to approximate the Newton's scheme in the inverse method. With this approximate model,

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Keneskyzy, K., та S. B. Yeskermes. "Метод машинного обучения для обратных задач теплопроводности". INTERNATIONAL JOURNAL OF INFORMATION AND COMMUNICATION TECHNOLOGIES 2, № 1(5) (2021): 59–64. http://dx.doi.org/10.54309/ijict.2021.05.1.008.

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Investigated in this work is the potential of carrying out inverse problems with linear and non-linear behavior using machine learning methods and the neural network method. With the advent of ma-chine learning algorithms it is now possible to model inverse problems faster and more accurately. In order to demonstrate the use of machine learning and neural networks in solving inverse problems, we propose a fusion between computational mechanics and machine learning. The forward problems are solved first to create a database. This database is then used to train the machine learning and neural ne

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13

Liu, Xian Xing, Jie Chen, Yi Du, and Kai Shi. "Model Reference Adaptive Control of HMB Based on PSO-LS-SVM Inverse." Applied Mechanics and Materials 416-417 (September 2013): 870–75. http://dx.doi.org/10.4028/www.scientific.net/amm.416-417.870.

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To realize the hybrid magnetic bearing (HMB) nonlinear decoupling control with high precision, a strategy of model reference adaptive control (MRAC) based on the least square support vector machine (LS-SVM) inverse is proposed. After analyzing the reversibility of HMB, the LS-SVM regression theory is used to identify the inverse model, the parameters of LS-SVM are optimized by Particle Swarm Optimization (PSO) algorithm. Then the nonlinear system is transformed into a pseudo-linear system by connecting the optimized the inverse model and the original unit. MRAC is designed to realize the compo

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14

Fosgerau, Mogens, Julien Monardo, and André de Palma. "The Inverse Product Differentiation Logit Model." American Economic Journal: Microeconomics 16, no. 4 (2024): 329–70. http://dx.doi.org/10.1257/mic.20210066.

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We introduce the inverse product differentiation logit (IPDL) model, a micro-founded inverse market share model for differentiated products that captures market segmentation according to one or more characteristics. The IPDL model generalizes the nested logit model to allow richer substitution patterns, including complementarity in demand, and can be estimated by linear instrumental variable regression with market-level data. Furthermore, we provide Monte Carlo experiments comparing the IPDL model to the workhorse empirical models of the literature. Lastly, we demonstrate the empirical perform

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15

Lyubchyk, Leonid M. "Disturbance Rejection in Linear Discrete Multivariable Systems: Inverse Model Approach." IFAC Proceedings Volumes 44, no. 1 (2011): 7921–26. http://dx.doi.org/10.3182/20110828-6-it-1002.02121.

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16

Alexander, Michael A., Ludmila Matrosova, Cécile Penland, James D. Scott, and Ping Chang. "Forecasting Pacific SSTs: Linear Inverse Model Predictions of the PDO." Journal of Climate 21, no. 2 (2008): 385–402. http://dx.doi.org/10.1175/2007jcli1849.1.

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Abstract A linear inverse model (LIM) is used to predict Pacific (30°S–60°N) sea surface temperature anomalies (SSTAs), including the Pacific decadal oscillation (PDO). The LIM is derived from the observed simultaneous and lagged covariance statistics of 3-month running mean Pacific SSTA for the years 1951–2000. The model forecasts exhibit significant skill over much of the Pacific for two to three seasons in advance and up to a year in some locations, particulary for forecasts initialized in winter. The predicted and observed PDO are significantly correlated at leads of up to four seasons, fo

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17

Martinez-Villalobos, Cristian, Daniel J. Vimont, Cécile Penland, Matthew Newman, and J. David Neelin. "Calculating State-Dependent Noise in a Linear Inverse Model Framework." Journal of the Atmospheric Sciences 75, no. 2 (2018): 479–96. http://dx.doi.org/10.1175/jas-d-17-0235.1.

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Abstract The most commonly used version of a linear inverse model (LIM) is forced by state-independent noise. Although having several desirable qualities, this formulation can only generate long-term Gaussian statistics. LIM-like systems forced by correlated additive–multiplicative (CAM) noise have been shown to generate deviations from Gaussianity, but parameter estimation methods are only known in the univariate case, limiting their use for the study of coupled variability. This paper presents a methodology to calculate the parameters of the simplest multivariate LIM extension that can gener

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18

Zhou, Junqiang, Marcello Canova, and Andrea Serrani. "Predictive inverse model allocation for constrained over-actuated linear systems." Automatica 67 (May 2016): 267–76. http://dx.doi.org/10.1016/j.automatica.2016.01.045.

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19

Wang, Lichun, and Lawrence Pettit. "Linear Bayes estimators applied to the inverse Gaussian lifetime model." Journal of Systems Science and Complexity 29, no. 6 (2016): 1683–92. http://dx.doi.org/10.1007/s11424-016-5030-7.

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20

Lavoie, Francis B., Alyssa Langlet, Koji Muteki, and Ryan Gosselin. "Likelihood Maximization Inverse Regression: A novel non-linear multivariate model." Chemometrics and Intelligent Laboratory Systems 194 (November 2019): 103844. http://dx.doi.org/10.1016/j.chemolab.2019.103844.

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21

Babier, Aaron, Timothy C. Y. Chan, Taewoo Lee, Rafid Mahmood, and Daria Terekhov. "An Ensemble Learning Framework for Model Fitting and Evaluation in Inverse Linear Optimization." INFORMS Journal on Optimization 3, no. 2 (2021): 119–38. http://dx.doi.org/10.1287/ijoo.2019.0045.

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We develop a generalized inverse optimization framework for fitting the cost vector of a single linear optimization problem given multiple observed decisions. This setting is motivated by ensemble learning, where building consensus from base learners can yield better predictions. We unify several models in the inverse optimization literature under a single framework and derive assumption-free and exact solution methods for each one. We extend a goodness-of-fit metric previously introduced for the problem with a single observed decision to this new setting and demonstrate several important prop

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22

Uyar, Erol, and Lutfi Mutlu. "Modelling and Kinematic Analysis of a Built Up Linear Delta Robot." Applied Mechanics and Materials 186 (June 2012): 234–38. http://dx.doi.org/10.4028/www.scientific.net/amm.186.234.

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In this paper kinematic analysis of a 3-PUU translational parallel manipulator (TPM) is made by creating the forward and inverse Kinematic solutions. For a given position, control of the end effecter is then realized by using the calculated inverse kinematic parameters as reference values. For kinematic analysis relevant equations are derived from geometrical vector relations. For the forward and inverse kinematic solutions of the non-linear model a MATLAB based iterative algorithm is developed and the inverse kinematic solutions of limbs, are then used to control the end effecter position thr

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23

Turetsky, Vladimir. "Two Inverse Problems Solution by Feedback Tracking Control." Axioms 10, no. 3 (2021): 137. http://dx.doi.org/10.3390/axioms10030137.

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Two inverse ill-posed problems are considered. The first problem is an input restoration of a linear system. The second one is a restoration of time-dependent coefficients of a linear ordinary differential equation. Both problems are reformulated as auxiliary optimal control problems with regularizing cost functional. For the coefficients restoration problem, two control models are proposed. In the first model, the control coefficients are approximated by the output and the estimates of its derivatives. This model yields an approximating linear-quadratic optimal control problem having a known

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24

Shafiq, M., and M. Haseebuddin. "U-Model-Based Internal Model Control for Non-Linear Dynamic Plants." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 219, no. 6 (2005): 449–58. http://dx.doi.org/10.1243/095965105x33563.

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In this paper, a U-model in the internal model control (IMC) structure is used. The U-model is a control-oriented model applicable to a wide class of non-linear plants. It is a non-linear polynomial representation of the plant, which allows the use of well-established polynomial controller design methodologies. A learning rate parameter is introduced in the inverse finding computational algorithm in order to improve the convergence and stability properties. Computer simulation results and real-time experimental results are presented to show the effectiveness of the proposed method.

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25

Luh, G.-C., and C.-Y. Wu. "Inversion control of non-linear systems with an inverse NARX model identified using genetic algorithms." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 214, no. 4 (2000): 259–71. http://dx.doi.org/10.1243/0959651001540627.

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The inverse dynamics approach has been widely utilized in the control problem of various practical non-linear systems in recent years. This paper demonstrates a feedforward-feedback controller scheme of a non-linear plant whose dynamics are unknown and uncertain. The feedforward controller, an inverse NARX model (non-linear autoregressive model with exogenous inputs), provides only coarse control, whereas the feedback controller is used to handle unmodelled dynamics and disturbance. The inverse NARX model is derived by inverting the forward NARX model identified using genetic algorithms. A par

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26

Shipit’ko, Oleg, and Anatoly Kabakov. "Mapping of linear road features with the inverse visual detector observation model." Robotics and Technical Cybernetics 9, no. 3 (2021): 214–24. http://dx.doi.org/10.31776/rtcj.9307.

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The paper proposes an algorithm for mapping linear features detected on the roadway — road marking lines, curbs, road boundaries. The algorithm is based on a mapping method with an inverse observation model. An inverse observation model is proposed to take into account the spatial error of the linear feature visual detector. The influence of various parameters of the model on the resulting quality of mapping was studied. The mapping algorithm was tested on data recorded on an autonomous vehicle while driving at the test site. The quality of the mapping algorithm was assessed according to sever

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27

Sabaté Landman, Malena, Julianne Chung, Jiahua Jiang, Scot M. Miller, and Arvind K. Saibaba. "A joint reconstruction and model selection approach for large-scale linear inverse modeling (msHyBR v2)." Geoscientific Model Development 17, no. 23 (2024): 8853–72. https://doi.org/10.5194/gmd-17-8853-2024.

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Abstract. Inverse models arise in various environmental applications, ranging from atmospheric modeling to geosciences. Inverse models can often incorporate predictor variables, similar to regression, to help estimate natural processes or parameters of interest from observed data. Although a large set of possible predictor variables may be included in these inverse or regression models, a core challenge is to identify a small number of predictor variables that are most informative of the model, given limited observations. This problem is typically referred to as model selection. A variety of c

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28

Nie, Boxin, and Xiaojia Ran. "A Linear Discriminant Analysis model based on Sliced Inverse Regression lifting." Highlights in Science, Engineering and Technology 103 (June 26, 2024): 424–32. http://dx.doi.org/10.54097/07bdff79.

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Linear Discriminant Analysis is a dimension-reduction tool for high-dimensional data in the classical field of machine learning. However, for the binary classification problem in the case of high-dimensional prediction, the dimension of the dimension reduction space of the Linear Discriminant Analysis model cannot be greater than the number of categories, that is, it can only be reduced to one dimension, which will greatly lose the effective information of the original prediction variable for the response variable. Based on this, this paper proposes an improved Linear Discriminant Analysis mod

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29

Goutsias, John I., and Jerry M. Mendel. "Inverse problems in two‐dimensional acoustic media: A linear imaging model." Journal of the Acoustical Society of America 81, no. 5 (1987): 1471–85. http://dx.doi.org/10.1121/1.394500.

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30

Lou, Jiale, Terence J. O’Kane, and Neil J. Holbrook. "A Linear Inverse Model of Tropical and South Pacific Seasonal Predictability." Journal of Climate 33, no. 11 (2020): 4537–54. http://dx.doi.org/10.1175/jcli-d-19-0548.1.

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AbstractA multivariate linear inverse model (LIM) is developed to demonstrate the mechanisms and seasonal predictability of the dominant modes of variability from the tropical and South Pacific Oceans. We construct a LIM whose covariance matrix is a combination of principal components derived from tropical and extratropical sea surface temperature, and South Pacific Ocean vertically averaged temperature anomalies. Eigen-decomposition of the linear deterministic system yields stationary and/or propagating eigenmodes, of which the least damped modes resemble El Niño–Southern Oscillation (ENSO) a

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31

Soufflet, Laurent, and Peter H. Boeijinga. "Linear Inverse Solutions: Simulations from a Realistic Head Model in MEG." Brain Topography 18, no. 2 (2005): 87–99. http://dx.doi.org/10.1007/s10548-005-0278-6.

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32

Filelis-Papadopoulos, Christos K., and George A. Gravvanis. "A class of generic factored and multi-level recursive approximate inverse techniques for solving general sparse systems." Engineering Computations 33, no. 1 (2016): 74–99. http://dx.doi.org/10.1108/ec-12-2014-0261.

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Purpose – The purpose of this paper is to propose novel factored approximate sparse inverse schemes and multi-level methods for the solution of large sparse linear systems. Design/methodology/approach – The main motive for the derivation of the various generic preconditioning schemes lies to the efficiency and effectiveness of factored preconditioning schemes in conjunction with Krylov subspace iterative methods as well as multi-level techniques for solving various model problems. Factored approximate inverses, namely, Generic Factored Approximate Sparse Inverse, require less fill-in and are c

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33

VanDecar, John C., and Roel Snieder. "Obtaining smooth solutions to large, linear, inverse problems." GEOPHYSICS 59, no. 5 (1994): 818–29. http://dx.doi.org/10.1190/1.1443640.

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It is not uncommon now for geophysical inverse problems to be parameterized by [Formula: see text] to [Formula: see text] unknowns associated with upwards of [Formula: see text] to [Formula: see text] data constraints. The matrix problem defining the linearization of such a system (e.g., [Formula: see text]m = b) is usually solved with a least‐squares criterion [Formula: see text]. The size of the matrix, however, discourages the direct solution of the system and researchers often turn to iterative techniques such as the method of conjugate gradients to obtain an estimate of the least‐squares

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34

Li, Youbing, Zhenning Zhu, and Zhixian Zhong. "Decoupling control of electromagnetic bearing rigid rotor system based on IGWO and linear active disturbance rejection inverse system." Journal of Physics: Conference Series 3024, no. 1 (2025): 012029. https://doi.org/10.1088/1742-6596/3024/1/012029.

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Abstract This study proposes a decoupling control strategy integrating an improved grey wolf optimization algorithm (IGWO) and a linear active disturbance rejection inverse system for rigid rotor system control in electromagnetic bearings. Initially, a mathematical model of the radial four-degree-of-freedom electromagnetic bearing rotor system is developed, and the system is decoupled into four pseudo-linear subsystems via the inverse system method. To address the model dependency of the inverse system, a linear active disturbance rejection controller (LADRC) is designed to regulate the decoup

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Weglein, Arthur B., Haiyan Zhang, Adriana C. Ramírez, Fang Liu, and Jose Eduardo Lira. "Clarifying the underlying and fundamental meaning of the approximate linear inversion of seismic data." GEOPHYSICS 74, no. 6 (2009): WCD1—WCD13. http://dx.doi.org/10.1190/1.3256286.

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Linear inversion is defined as the linear approximation of a direct-inverse solution. This definition leads to data requirements and specific direct-inverse algorithms, which differ with all current linear and nonlinear approaches, and is immediately relevant for target identification and inversion in an elastic earth. Common practice typically starts with a direct forward or modeling expression and seeks to solve a forward equation in an inverse sense. Attempting to solve a direct forward problem in an inverse sense is not the same as solving an inverse problem directly. Distinctions include

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Ji, Liya, Zhefan Rao, Sinno Jialin Pan, Chenyang Lei, and Qifeng Chen. "A Diffusion Model with State Estimation for Degradation-Blind Inverse Imaging." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 3 (2024): 2471–79. http://dx.doi.org/10.1609/aaai.v38i3.28023.

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Solving the task of inverse imaging problems can restore unknown clean images from input measurements that have incomplete information. Utilizing powerful generative models, such as denoising diffusion models, could better tackle the ill-posed issues of inverse problems with the distribution prior of the unknown clean images. We propose a learnable state-estimator-based diffusion model to incorporate the measurements into the reconstruction process. Our method makes efficient use of the pre-trained diffusion models with computational feasibility compared to the conditional diffusion models, wh

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37

Gao, Xudong, and Sheng Zhang. "Inverse scattering transform for a new non-isospectral integrable non-linear AKNS model." Thermal Science 21, suppl. 1 (2017): 153–60. http://dx.doi.org/10.2298/tsci17s1153g.

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Constructing integrable systems and solving non-linear partial differential equations are important and interesting in non-linear science. In this paper, Ablowitz-Kaup-Newell-Segur (AKNS)?s linear isospectral problem and its accompanied time evolution equation are first generalized by embedding a new non-isospectral parameter whose varying with time obeys an arbitrary smooth enough function of the spectral parameter. Based on the generalized AKNS linear problem and its evolution equation, a new non-isospectral Lax integrable non-linear AKNS model is then derived. Furthermore, exact solutions o

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Lounici, Yacine, Youcef Touati, Smail Adjerid, Djamel Benazzouz, and Billal Nazim Chebouba. "A novel fault-tolerant control strategy based on inverse bicausal bond graph model in linear fractional transformation." Advances in Mechanical Engineering 13, no. 11 (2021): 168781402110598. http://dx.doi.org/10.1177/16878140211059878.

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This article presents the development of a novel fault-tolerant control strategy. For this task, a bicausal bond graph model-based scheme is designed to generate online information to the inverse controller about the faults estimation. Secondly, a new approach is proposed for the fault-tolerant control based on the inverse bicausal bond graph in linear fractional transformation form. However, because of the time delay for fault estimation, the PI controller is used to reduce the error before the fault is estimated. Hence, the required input that compensates the fault is the sum of the control

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Lobo, Daniel, Mauricio Solano, George A. Bubenik, and Michael Levin. "A linear-encoding model explains the variability of the target morphology in regeneration." Journal of The Royal Society Interface 11, no. 92 (2014): 20130918. http://dx.doi.org/10.1098/rsif.2013.0918.

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A fundamental assumption of today's molecular genetics paradigm is that complex morphology emerges from the combined activity of low-level processes involving proteins and nucleic acids. An inherent characteristic of such nonlinear encodings is the difficulty of creating the genetic and epigenetic information that will produce a given self-assembling complex morphology. This ‘inverse problem’ is vital not only for understanding the evolution, development and regeneration of bodyplans, but also for synthetic biology efforts that seek to engineer biological shapes. Importantly, the regenerative

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Ungureanu, Liviu Marian, Adriana Comanescu, and Dinu Comanescu. "Some Characteristics of the 3($P$) Parallel Manipulator Inverse Model." Applied Mechanics and Materials 555 (June 2014): 306–11. http://dx.doi.org/10.4028/www.scientific.net/amm.555.306.

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A 3($P$) parallel manipulator with three degrees of mobility may be used in different purposes, such as a spatial flight simulator. The same equations but with different unknowns characterize its direct and inverse positional model. By adopting the Euler angles for the mobile platform the inverse positional model is simplified. There are determined the parameters of the linear actuators, which are verified for different situations by graphical simulation in a suitable environment.

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Damayanti CR, Mey, and Teti Sofia Yanti. "Regresi Poisson Invers Gaussian (PIG) untuk Pemodelan Jumlah Kasus Pneumonia pada Balita di Provinsi Jawa Tengah Tahun 2019." Jurnal Riset Statistika 1, no. 2 (2022): 143–51. http://dx.doi.org/10.29313/jrs.v1i2.523.

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Abstract. Poisson regression is a non-linear regression model used on non-negative count or discrete data. Poisson regression is included in the Generalized Linear Model (GLM). In Poisson regression there is an assumption that must be met, that is equidispersion where the value of the variance in the response variable (Y) must be the same as the average value. If in Poisson regression modeling there is an overdispersion or underdispersion and it is ignored, the test will be less accurate because the standard error value will be underestimated. Poisson Inverse Gaussian Regression Model (PIG) ca

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Zhang, Yi, and Gongsheng Li. "A Simplified Fractional Seir Epidemic Model and Unique Inversion of the Fractional Order." WSEAS TRANSACTIONS ON MATHEMATICS 21 (March 23, 2022): 113–18. http://dx.doi.org/10.37394/23206.2022.21.17.

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A simplified linear time-fractional SEIR epidemic system is set forth, and an inverse problem of determining the fractional order is discussed by using the measurement at one given time. By the Laplace transform the solution to the forward problem is obtained, by which the inverse problem is transformed to a nonlinear algebraic equation. By choosing suitable model parameters and the measured time, the nonlinear equation has a unique solution by the monotonicity of the Mittag-Lellfer function. Theoretical testification is presented to demonstrate the unique solvability of the inverse problem.

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Shi, Jianfei, Mingze Li, Baihong Tong, and Zhenlin Guo. "A New Control Strategy for Greenhouse Environment Control System Based on Inverse Model." International Journal of Heat and Technology 40, no. 5 (2022): 1271–76. http://dx.doi.org/10.18280/ijht.400520.

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The greenhouse environment control system is a type of non-linear system since the temperature and humidity of the system are highly coupled. Besides, the time lag of the temperature and humidity control process is large, so it’s quite difficult to linearize and decouple the temperature and humidity of the system. To cope with this issue, this paper proposed a novel control strategy for greenhouse environment control system based on Back Propagation Neural Network (BPNN) and inverse model, the proposed method can perform inverse identification on the temperature and humidity control system to

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Sahinkaya, M. N. "Virtual non-linear disturbance observer by dual inverse dynamic modelling." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 221, no. 6 (2007): 677–88. http://dx.doi.org/10.1243/0954406jmes581.

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A method to predict unknown external disturbances and modelling errors for trajectory tracking of non-linear systems is presented. The technique involves dual inverse non-linear dynamics modelling based on Lagrangian dynamics and the use of redundant coordinates incorporating Lagrange multipliers. The first inverse dynamics model is a conventional formulation where the desired trajectory is used to calculate the required control inputs. The second inverse dynamics model treats the measured response as the desired motion, and calculates the inputs that would be required to achieve the measured

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Perkins, Walter A., and Gregory J. Hakim. "Reconstructing paleoclimate fields using online data assimilation with a linear inverse model." Climate of the Past 13, no. 5 (2017): 421–36. http://dx.doi.org/10.5194/cp-13-421-2017.

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Abstract. We examine the skill of a new approach to climate field reconstructions (CFRs) using an online paleoclimate data assimilation (PDA) method. Several recent studies have foregone climate model forecasts during assimilation due to the computational expense of running coupled global climate models (CGCMs) and the relatively low skill of these forecasts on longer timescales. Here we greatly diminish the computational cost by employing an empirical forecast model (linear inverse model, LIM), which has been shown to have skill comparable to CGCMs for forecasting annual-to-decadal surface te

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Clarke, Shanelle G., Sooyung Byeon, and Inseok Hwang. "A Low Complexity Approach to Model-Free Stochastic Inverse Linear Quadratic Control." IEEE Access 10 (2022): 9298–308. http://dx.doi.org/10.1109/access.2022.3144933.

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Gennarelli, Gianluca, Giovanni Ludeno, Noviello Carlo, Ilaria Catapano, and Francesco Soldovieri. "The Role of Model Dimensionality in Linear Inverse Scattering from Dielectric Objects." Remote Sensing 14, no. 1 (2022): 222. http://dx.doi.org/10.3390/rs14010222.

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This paper deals with 3D and 2D linear inverse scattering approaches based on the Born approximation, and investigates how the model dimensionality influences the imaging performance. The analysis involves dielectric objects hosted in a homogenous and isotropic medium and a multimonostatic/multifrequency measurement configuration. A theoretical study of the spatial resolution is carried out by exploiting the singular value decomposition of 3D and 2D scattering operators. Reconstruction results obtained from synthetic data generated by using a 3D full-wave electromagnetic simulator are reported

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Carcreff, Ewen, Sebastien Bourguignon, Jerome Idier, and Laurent Simon. "A linear model approach for ultrasonic inverse problems with attenuation and dispersion." IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control 61, no. 7 (2014): 1191–203. http://dx.doi.org/10.1109/tuffc.2014.3018.

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Cavaterra, Cecilia, and Maurizio Grasselli. "On an Inverse Problem for a Model of Linear Viscoelastic Kirchhoff Plate." Journal of Integral Equations and Applications 9, no. 3 (1997): 179–218. http://dx.doi.org/10.1216/jiea/1181076012.

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Ahani, Alireza, and Mohammad Javad Ketabdari. "Alternative approach for dynamic-positioning thrust allocation using linear pseudo-inverse model." Applied Ocean Research 90 (September 2019): 101854. http://dx.doi.org/10.1016/j.apor.2019.101854.

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