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The dynamics of the lynx–hare system: an application of the Lotka–Volterra model

Biophysics (Russian Federation) 2016年第1期61卷 149-154页

作者： Nedorezov, L.V. Research Center for Interdisciplinary Environmental Cooperation Russian Academy of Sciences nab. Kutuzova 14 St. Petersburg 191187 Russian Federation

The Lotka–Volterra model of predator–prey dynamics was used for approximation of the wellknown empirical time series on the lynx–hare system in Canada that was collected by the Hudson Bay Company in 1845–1935. The model was assumed to demonstrate satisfactory data approximation if the sets of deviations of the model and empirical data for both time series satisfied a number of statistical criteria (for the selected significance level). The frequency distributions of deviations between the theoretical (model) trajectories and empirical datasets were tested for symmetry (with respect to the Y-axis;the Kolmogorov–Smirnov and Lehmann–Rosenblatt tests) and the presence or absence of serial correlation (the Swed–Eisenhart and “jumps up–jumps down” tests). The numerical calculations show that the set of points of the space of model parameters, when the deviations satisfy the statistical criteria, is not empty and, consequently, the model is suitable for describing empirical data. © 2016, Pleiades Publishing, Inc.

关键词： deviation analysis model parameter estimation the Lotka–Volterra model

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Adaptive modelling of Fed-batch Processes with Extreme Learning Machine and Recursive Least Square Technique 12

Adaptive Modelling of Fed-batch Processes with Extreme Learn...

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12th International Conference on Agents and Artificial Intelligence (ICAART)

作者： Alli, Kazeem Zhang, Jie Newcastle Univ Sch Engn Newcastle Upon Tyne NE1 7RU Tyne & Wear England

ISBN: (纸本)9789897583957

This paper presents a new strategy to integrate extreme learning machine (ELM) with recursive least square (RLS) technique for the adaptive modelling of fed-batch processes that are subject to unknown disturbances. ELM has the advantage of fast training and good generalization. ELM is effective in modelling nonlinear processes but faces problems when the modelled process is time varying due to the presence of unknown disturbances or process condition drift. The RLS can adapt to current process operation by recursively solving the least square problem in the considered model. The RLS estimation algorithm nullifies the model plant mismatches caused by the unknown disturbances through correction of parameter estimates at each iteration. The offline trained output layer weights of an ELM are used as the initial values in parameter estimation in RLS, which are being updated after each batch run using RLS. The proposed strategy is tested on an isothermal semi-batch reactor. The results obtained show that the proposed batch to batch adaptive modelling technique is very effective.

关键词： Extreme Learning Machine Neural Networks Fed-batch Processes Recursive Least Square model parameter estimation Optimization Control

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An integrated approach to apdative anticipatory traffic control and parameter estimation

An integrated approach to apdative anticipatory traffic cont...

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International Conference on models and Technologies for Intelligent Transportation Systems (MT-ITS)

作者： Huang, Wei Tampere, Chris M. J. Viti, Francesco Katholieke Univ Leuven Leuven Mobil Res Ctr CIB Leuven Belgium Univ Luxembourg Fac Sci Technol & Commun Luxembourg Luxembourg

ISBN: (纸本)9789633131428

This paper develops an integrated approach to combine adaptive anticipatory network traffic control and model parameter estimation. The main objective is to complete an iterative control optimization method that has been proposed to drive the traffic system towards its true optimal operating point despite model uncertainty [1], in which the underlying network equilibrium model is imperfectly calibrated. In this integrated framework, two objectives are fulfilled, i.e. designing optimal reality-tracking control, and identifying a better model description of the real system at its current operating condition. The interaction among control setting update, model bias update and model parameter update, as well as its influence on the performance of the integrated framework is analyzed. Numerical tests verify the effectiveness of the proposed integrated approach in jointly performing adaptive signal control and route choice behavioral parameter estimation. It is demonstrated that the parameter estimation reduces the discrepancy between model and measured reality, while at the same time guiding the traffic network towards optimal anticipatory control despite mismatch between reality and the model used to anticipate the response of the users.

关键词： anticipatory traffic control adaptive signal control model uncertainty iterative optimization model bias correction model parameter estimation

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Analysis of a model Reduction Method (D-QSSA) applied to a Class of Biochemical Networks 11th

Analysis of a Model Reduction Method (D-QSSA) applied to a C...

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11th IFAC Symposium on Biological and Medical Systems (BMS)

作者： Papacek, Stepan Rehak, Branislav Lynnyk, Volodymyr Lynnyk, Anna Czech Acad Sci Inst Informat Theory & Automat Prague Czech Republic

This paper is aimed to develop and test one novel and unexplored enhancement of the classical model reduction method applied to a class of biochemical networks. Both methods, being (i) the standard quasi-steady-state approximation (QSSA), and (ii) the so-called delayed-QSSA methods are extensively presented. Specially, the numerical issues related to the setting of constant delays are discussed. Finally, for one slightly modified version of an enzyme-substrate reaction network (Michaelis-Menten kinetics), the comparison of the full non-reduced system behavior with respective variants of reduced model is presented and future prospects are proposed. Copyright (C) 2021 The Authors.

关键词： biochemical reaction network model reduction D-QSSA method model parameter estimation

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Building Interpretable and Parsimonious Fuzzy models using a Multi-Objective Approach

Building Interpretable and Parsimonious Fuzzy Models using a...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) / IEEE World Congress on Computational Intelligence (IEEE WCCI) / International Joint Conference on Neural Networks (IJCNN) / IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Fuchs, Caro Kaymak, Uzay Nobile, Marco S. Eindhoven Univ Technol Jheronimus Acad Data Sci Shertogenbosch Netherlands Ca Foscari Univ Venice Dept Environm Sci Informat & Stat Venice Italy Eindhoven Univ Technol Dept Ind Engn & Innovat Sci Eindhoven Netherlands Interdisciplinary Res Ctr Bicocca Bioinformat Biostat & Bioimaging B4 Milan Italy

ISBN: (数字)9781665467100

ISBN: (纸本)9781665467100

Nowadays, the growing amounts of collected data enable the training of machine learning models that can be used to extract insights from the data and make better-informed decisions. Among the possible models that can be learned from data are fuzzy rule-based models, which are transparent and enable when properly designed- interpretable artificial intelligence. One of the requirements of interpretability is a simple model structure, which can be achieved by performing feature selection and by limiting the number of rules in the model. However, the chosen feature set and the number of rules may interact and strongly affect the model's accuracy. In this study, we employ techniques from the field of evolutionary computation to perform feature and rule number selection simultaneously. To ensure the developed models do not only perform well but are also interpretable and have good generalization capabilities, we adopt a multi-objective approach in which we train the models focusing on three objectives: performance, complexity, and model stability. In this way, we strive to develop simple, well-performing parsimonious fuzzy models. We show the effectiveness of our approach on three benchmark data sets.

关键词： Fuzzy model Feature selection model parameter estimation Explainable AI (XAI) Multi-objective optimization

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Textured Images Segmentation Based on Hierarchical Finite-valued Gibbs model 13

Textured Images Segmentation Based on Hierarchical Finite-va...

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13th International Scientific-Technical Conference on Actual Problems of Electronics Instrument Engineering (APEIE)

作者： Vasyukov, Vasily N. Zaitseva, Anna Yu. Novosibirsk State Tech Univ Novosibirsk Russia

ISBN: (纸本)9781509040698

The paper introduces several variants of hierarchical Gibbs model applied for solving the problem of textured images segmentation. Binary contour preparation and three-valued texture preparation are used to describe textured images. We demonstrate the performance of the developed algorithms with examples of practical processing real textured images.

关键词： Markov random fields textured image segmentation Gibbs distribution hierarchical model finite-valued Gibbs field model parameter estimation

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modeling of COVID-19 propagation with compartment models

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Mathematische Semesterberichte 2021年第2期68卷 181-219页

作者： Bärwolff, Günter Inst. f. Math. Technische Universität Berlin Str. des 17. Juni 136 Berlin 10623 Germany

The current pandemic is a great challenge for several research areas. In addition to virology research, mathematical models and simulations can be a valuable contribution to the understanding of the dynamics of the pandemic and can give recommendations to both physicians and politicians. In this paper we give an overview about mathematical models to describe the pandemic by differential equations. As a matter of principle the historic origin of the epidemic growth models will be remembered. Moreover we discuss models for the actual pandemic of 2020/2021. This will be done based on actual data of people infected with COVID-19 from the European Centre for Disease Prevention and Control (ECDC), input parameters of mathematical models will be determined and applied. These parameters will be estimated for the UK, Italy, Spain, and Germany and used in a SIR-type model. As a basis for the model’s calibration, the initial exponential growth phase of the COVID-19 pandemic in the named countries is used. Strategies for the commencing and ending of social and economic shutdown measures are discussed. To respect heterogeneity of the people density in the different federal states of Germany diffusion effects are considered. © 2021, Springer-Verlag GmbH Deutschland, ein Teil von Springer Nature.

关键词： COVID-19/SARS-CoV‑2 Mathematical epidemiology model parameter estimation SIR-type model

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An Online parameter estimation Method Based on Adaptive Unscented Kalman Filter for Unmanned Surface Vessel 34

An Online Parameter Estimation Method Based on Adaptive Unsc...

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34th Chinese Control and Decision Conference (CCDC)

作者： Shen, Han Lv, Yuezu Zhou, Jun Wang, Linan Feng, Yuting Southeast Univ Sch Cyber Sci & Engn Nanjing 211189 Peoples R China Beijing Inst Technol Adv Res Inst Multidisciplinary Sci Beijing 100081 Peoples R China Beijing Inst Technol Yangtze Delta Reg Acad Jiaxing 314000 Peoples R China Southeast Univ Sch Math Nanjing 211189 Peoples R China China Acad Space Technol Qian Xuesen Lab Space Technol Beijint 100094 Peoples R China

ISBN: (纸本)9781665478960

In this paper, an online parameter estimation method for unmanned surface vessels (USVs) is designed. The main idea is to establish an augmented system by viewing the parameters as system states, and then estimate the full states of the augmented system by using adaptive unscented Kalman filter (AUKF). Nine parameters including the inertial effects, the damping, the thrust allocation, and the current velocity can be online estimated accurately based on the measurements from real-time kinematic (RTK) Global Positioning System (GPS) and inertial measurement unit (IMU). The trajectory tracking control is further studied in the presence of input constraints, where the model predictive control (MPC) is introduced. The simulation results of parameter estimation demonstrate the effectiveness of the proposed method.

关键词： Unmanned surface vessel model parameter estimation Adaptive unscented Kalman filter model predictive control

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In-Vehicle Roundabout Perception Supported by A Priori Map Data 18

In-Vehicle Roundabout Perception Supported by A Priori Map D...

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18th IEEE International Conference on Intelligent Transportation Systems

作者： Raaijmakers, Marvin Bouzouraa, Mohamed Essayed Audi AG Predev Automated Driving Syst Ingolstadt Germany

ISBN: (纸本)9781467365963

In order to drive through a roundabout an automated vehicle needs to know the roundabout's geometry relative to the vehicle itself. In this work we present a model describing the geometry of a roundabout and a perception system that computes the parameters of this model relative to the ego vehicle before the ego vehicle enters the roundabout. Only a part of the model's parameters are estimated using on-board sensor observations. The complete roundabout geometry is computed by aligning a detailed geometrical description from an a priori digital map with the estimated parameters. For this purpose a regular navigation map is extended with geometric information about roundabouts. This extended map data is not georeferenced and the presented approach does not require high accuracy global localization or any additional landmarks to be stored in the map. The system's performance has been evaluated based on ground truth data.

关键词： estimation theory geometry mobile robots object detection parameter estimation path planning road vehicles a priori digital map automated vehicle circle detection algorithm ego vehicle in-vehicle roundabout perception model parameter estimation navigation map on-board sensor observation roundabout geometry Detectors Geometry Global Positioning System Logic gates Roads Vehicles

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Augmented Sequential Bayesian Filtering for parameter and modeling Error estimation of Linear Dynamic Systems 37th

Augmented Sequential Bayesian Filtering for Parameter and Mo...

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37th International Modal Analysis Conference and Exposition (IMAC) on Structural Dynamics

作者： Song, Mingming Ebrahimian, Hamed Moaveni, Babak Tufts Univ Dept Civil & Environm Engn Medford MA 02155 USA SC Solut Inc Sunnyvale CA USA

ISBN: (纸本)9783030120757;9783030120740

In this paper an augmented sequential Bayesian filtering approach is proposed for parameter and modeling error estimation of linear dynamic systems of civil structures using time domain input-output data through a sequential maximum a posteriori (MAP) estimation approach, which is similar to Kalman filtering method. However, in the application of existing Kalman filters, the estimation of modeling errors is rarely considered. Unlike traditional Kalman filter which provides state estimation at every time step, the proposed filtering approach estimates the parameter and modeling error on a windowing basis, i.e., the input and output data are divided into windows for estimation which would save computation burden. The analytical derivation of the proposed augmented sequential Bayesian filtering method is first presented, and then the method is verified through a numerical case study of a 3-story building model. An earthquake excitation is used as the input and the acceleration time history response of the building model is simulated. The simulated response is then polluted with different levels of Gaussian white noise to account for the measurement noise. The simulated response is used as the measured data for calibrating another 3-story shear building model which is different from the original model for simulation. modeling errors are introduced in this shear building model including the shear building assumption, grouping strategy and boundary conditions. The augmented sequential Bayesian filtering approach is applied to estimate the model parameters and modeling error. The performance of the proposed method is studied with respect to modeling errors, the number of sensors and the level of noise.

关键词： Sequential Bayesian filtering model parameter estimation modeling error estimation Uncertainty quantification

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