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Automation & Control Systems
Hefu Ye, Yongduan Song
Summary: This paper presents a control method for achieving prescribed-time regulation of strict-feedback-like systems in the presence of unknown yet time-varying control gains and mismatched uncertainties. The developed control algorithms are capable of full-state regulation within a preset time independent of initial condition and any other design parameter. The efficiency of the proposed method is verified through numerical examples.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2021)
Article
Economics
Evan Piermont
Summary: This article examines the structure of random choice resulting from random expected utility maximization and a tie-breaking rule. It provides a partial identification result and a constructive approach to identifying the consistent random utility model. The concept of choice capacities is introduced and axiomatized to translate arbitrary random expected utility models into choice behavior.
JOURNAL OF ECONOMIC THEORY
(2022)
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Automation & Control Systems
Jeng-Tze Huang, Yu-Wei Jiang
Summary: This paper proposes a composite adaptive neural control method for uncertain strict-feedback systems, which improves stability and tracking performance through adaptive controllers, update algorithms, and state filters.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2023)
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Computer Science, Information Systems
Yumei Sun, Lei Zhang
Summary: This paper presents a fuzzy logic adaptive fixed-time control method for nonlinear switched system, addressing the issue of completely uncertain system functions by developing a new criterion of fixed-time stability. The proposed adaptive laws do not satisfy linear differential equations but nonlinear ones, and simulation results confirm the feasibility of the algorithms presented.
INFORMATION SCIENCES
(2021)
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Computer Science, Artificial Intelligence
Zhuwu Shao, Yujuan Wang, Xiang Chen
Summary: In this paper, an online solution based on neural network is proposed for real-time reconstruction and prediction of uncertain target trajectory. By introducing a novel time-varying scaling function and translation function, the proposed control scheme achieves global stability and is able to track the target and converge to a prescribed small set in the presence of uncertainties and unknown control directions.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Automation & Control Systems
Yibo Zhang, Dan Wang, Zhouhua Peng, Tieshan Li
Summary: This paper investigates the distributed containment maneuvering problem for uncertain nonlinear multiagent systems, and develops a containment maneuvering controller through a modular design method enabling decoupled estimation and control. The stability and boundedness of errors in the closed-loop system are proven.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2021)
Article
Mathematics, Applied
Emil Catinas
Summary: This note identifies four distinct classes of strict superlinear order: weak, medium, strong, and mixed. The speed of the first three classes is increasingly faster than linear convergence, while the speed of the mixed class cannot be assessed. Numerical evaluation can determine which class a given sequence belongs to.
NUMERICAL ALGORITHMS
(2023)
Article
Mathematics, Applied
Michael Damron, Jack Hanson, Philippe Sosoe
Summary: The study presents the first nontrivial upper bound for the chemical distance exponent in two-dimensional critical percolation, showing a relationship between the expected length of the shortest horizontal crossing path and the three-arm probability. Additionally, a strict upper bound for the exponent in site percolation on the triangular lattice is obtained, building on previous strategies with new iterative schemes and deviation inequalities.
COMMUNICATIONS ON PURE AND APPLIED MATHEMATICS
(2021)
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Computer Science, Information Systems
Chien-Ming Chen, Lili Chen, Wensheng Gan, Lina Qiu, Weiping Ding
Summary: This paper introduces a novel algorithm, UHUOPM, for mining high-utility occupancy patterns in uncertain databases, dividing user preferences into three factors: support, probability, and utility occupancy. The algorithm utilizes PUO-list and PFU-table to reduce memory cost and time consumption while also constructing a support count tree (SC-tree) for pruning the search space. Substantial experiments were conducted to evaluate the algorithm's performance on real-life and synthetic datasets, focusing on effectiveness and efficiency.
INFORMATION SCIENCES
(2021)
Article
Operations Research & Management Science
M. Chinaie, F. Fakhar, M. Fakhar, H. R. Hajisharifi
Summary: In this article, the concepts of l-transfer lower continuity and q-level intersectional closure are introduced for set-valued mappings with respect to the lower set less relation. The existence results for strict weak l-efficient solutions of such set-valued mappings are obtained. Moreover, some existence results for nonconvex set optimization problems are proven using asymptotic analysis tools in Banach spaces equipped with a Hausdorff topology sigma coarser than the norm topology.
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Biochemical Research Methods
Carlos Correa, Mark Ho, Frederick Callaway, Nathaniel Daw, Thomas Griffiths
Summary: People solve complex tasks by decomposing them into simpler subtasks. This research proposes a model that explains human task decomposition strategies based on rational trade-offs between solution value and planning cost. The framework is able to justify existing heuristics and predict human responses in a large-scale experiment.
PLOS COMPUTATIONAL BIOLOGY
(2023)
Article
Engineering, Multidisciplinary
Jaleleddine Ben Amor, Souad Chennaf
Summary: This paper studies the kurtosis of an uncertain random variable and its application in portfolio selection. The concept of kurtosis for uncertain random variables is introduced and some significant properties are extracted. A portfolio selection model based on uncertain random mean-variance-skewness-kurtosis is presented, along with numerical examples and a comparative study to illustrate the main results.
JOURNAL OF INDUSTRIAL AND MANAGEMENT OPTIMIZATION
(2023)
Article
Engineering, Multidisciplinary
Jaleleddine Ben Amor, Souad Chennaf
Summary: This paper studies the kurtosis of uncertain random variables and its application in portfolio selection. By introducing concepts and deriving mathematical models, the authors provide a method for portfolio selection that meets different investors' requirements.
JOURNAL OF INDUSTRIAL AND MANAGEMENT OPTIMIZATION
(2023)
Article
Computer Science, Artificial Intelligence
Hong Cheng, Xiucai Huang, Hongwei Cao
Summary: This paper proposes a method to achieve asymptotic tracking control for uncertain nonlinear strict-feedback systems with unknown time-varying delays and unknown control direction. The Lyapunov-Krasovskii functional is used to deal with the time delays, and the neural network is applied to compensate for the unknown terms arising from the derivative of the Lyapunov-Krasovskii functional. An NN-based adaptive control scheme is constructed based on backstepping technique, and the output tracking error is ensured to converge to zero asymptotically. The proposed method settles the singularity issue commonly encountered in coping with time delay problems and improves the transient performance with proper choice of design parameters.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Jixing Lv, Xiaozhe Ju, Changhong Wang
Summary: To address the tracking problem of uncertain strict-feedback nonlinear systems, this study proposes a predefined-time backstepping controller that utilizes a Lyapunov-based predefined-time dynamic paradigm, a regulation function, and neural networks. The controller incorporates an adding-absolute-value technique to eliminate control singularity and ensures boundedness of system signals. The advantages of this approach include precise predefined convergence time, noise tolerance, reduced tracking error and control input magnitude, and continuous and nonsingular behavior. Experimental results on a single-link manipulator demonstrate the effectiveness and superiority of the proposed controller.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
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