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Impact Time Control Guidance Using Output Trajectory Shaping Method

JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2025年第4期48卷 961-967页

作者： Lee, Suwon Kim, Mingu Tekin, Raziye Kookmin Univ Dept Future Mobil 77 Jeongneung Ro Seoul 02707 South Korea Hansung Univ Div Mech & Elect Engn Seoul 02876 South Korea Roketsan Missiles Inc Tactical Missile Syst TR-06780 Ankara Turkiye

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Approximating M-matrix in Learning Directed Acyclic Graphs Using Methods Involve Semidefinite Matrix Constraints

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2025年第2期50卷 1329-1337页

作者： Al-Homidan, Suliman KFUPM Dept Math Dhahran 31261 Saudi Arabia KFUPM Ctr Smart Mobil & Logist Dhahran 31261 Saudi Arabia

The task of deducing directed acyclic graphs from observational data has gained significant attention recently due to its broad applicability. Consequently, connecting the log-det characterization domain with the set of M-matrices defined over the cone of positive definite matrices has emerged as a crucial approach in this field. However, experimentally collected data often deviates from the expected positive semidefinite structure due to introduced noise, posing a challenge in maintaining its physical structure. In this paper, we address this challenge by proposing four methods to reconstruct the initial matrix while maintaining its physical structure. Leveraging advanced techniques, including sequential quadratic programming (SQP), we minimize the impact of noise, ensuring the recovery of the reconstructed matrix. We provide a rigorous proof of convergence for the SQP method, highlighting its effectiveness in achieving reliable reconstructions. Through comparative numerical analyses, we demonstrate the effectiveness of our methods in preserving the original structure of the initial matrix, even in the presence of noise.

关键词： Alternating projection method Directed acyclic graphs M- matrix Positive semidefinite matrix Semi-definite programming sequential quadratic programming

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A Novel Design Ricker Wavelet Neural Networks for Heat Transfer in Maxwell Fluid Boundary Layer Flow with Viscous Dissipation over a Porous Stretchable Sheet

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ACTA APPLICANDAE MATHEMATICAE 2025年第1期197卷 1-30页

作者： Butt, Zeeshan Ikram Ahmad, Iftikhar Shoaib, Muhammad Hussain, Syed Ibrar Ilyas, Hira Raja, Muhammad Asif Zahoor Univ Gujrat Dept Math Gujrat 50700 Pakistan Yuan Ze Univ AI Ctr Taoyuan 320 Taiwan Univ Palermo Dipartimento Matemat & Informat Via Archirafi 34 I-90123 Palermo Italy Univ Houston Dept Math Houston TX 77204 USA Univ Chenab Dept Phys Sci Gujrat 50700 Pakistan Natl Yunlin Univ Sci & Technol Future Technol Res Ctr 123 Univ RdSect 3 Touliu 64002 Yunlin Taiwan

The current research is a revolution in the field of neural computation as a quite new stochastic technique based on Ricker wavelet neural networks (RWNNs) is developed to analyze the Maxwell fluid (Max-F) boundary layer flow (BLF) with heat and mass transfer effects over an elongating surface. The global and local search solvers used with RWNNs are genetic algorithms (GAs) and sequential quadratic programming (SQP) respectively to design a new algorithm i.e. RWNNs-GASQP. The transformed nonlinear system of ODEs is acquired using the physical model represented by the flow and then solved using RWNNs-GASQP solver. The obtained numerical form results are successfully compared with reference results acquired through the Adams technique. The accuracy, convergence and effectiveness of the designed solver are identified using numerous statistical and performance analyses.

关键词： Ricker wavelet Neural networks Maxwell fluid Genetic algorithms sequential quadratic programming

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Heat Transfer in Chemically Reactive Dual Diffusive Casson Nanofluid Flow: An Intelligent Computing Paradigm

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International Journal of Applied and Computational Mathematics 2025年第1期11卷 1-50页

作者： Butt, Zeeshan Ikram Ahmad, Iftikhar Raja, Muhammad Asif Zahoor Shoaib, Muhammad Department of Mathematics University of Gujrat Gujrat 50700 Pakistan Future Technology Research Centre National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou 64002 Taiwan Yuan Ze University AI Center Taoyuan 320 Taiwan

In this research-oriented study, dual diffusive Casson nanofluid stretching flow embedded in a Darcy-Forchheimer-type porous medium is scrutinized. A quite new computerized neuro-heuristic optimization technique based on Ricker wavelet neural networks fabricated through global and local solvers namely genetic algorithms and sequential quadratic programming respectively. The flow of the suggested fluid model is characterized by thermal radiation, chemical reaction and Robin conditions for the analysis of heat and mass transfer effects. The similarity approach simplifies the governing partial differential equations of the currently discussed flow model into a dimensionless nonlinear system of ordinary differential equations. This system of equations is then solved using the crafted solver and the obtained numerical outcomes are successfully compared with the reference solution compiled through Adam’s numerical technique. It is observed that an increase in the value of the Casson parameter diminishes the nanofluid velocity however this effect is reversed on the thermal profile. A detailed form convergence analysis of the novel design created solver is accomplished using various statistical performance operators. © The Author(s), under exclusive licence to Springer Nature India Private Limited 2024.

关键词： Casson nanofluid Genetic algorithm Neural networks Ricker wavelet Robin conditions sequential quadratic programming

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A NEW SQP-FILTER METHOD programming FOR SOLVING NONLINEAR PROBLEMS

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Journal of Computational Mathematics 2006年第5期24卷 609-634页

作者： Duoquan Li LSEC ICMSEC Academy of Mathematics and System Sciences Chinese Academy of Sciences Beijing 100080 China Graduate School of Chinese Academy of Sciences Beijing 100080 China)

In [4], Fletcher and Leyffer present a new method that solves nonlinear programming problems without a penalty function by SQP-Filter algorithm. It has attracted much attention due to its good numerical results. In this paper we propose a new SQP-Filter method which can overcome Maratos effect more effectively. We give stricter acceptant criteria when the iterative points are far from the optimal points and looser ones vice-versa. About this new method, the proof of global convergence is also presented under standard assumptions. Numerical results show that our method is efficient.

关键词： Nonlinear programming sequential quadratic programming Filter Restoration phase Maratos affects Global convergence Multi-objective optimization quadratic programming subproblem.

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Gaussian process model predictive control of unknown non-linear systems

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IET CONTROL THEORY AND APPLICATIONS 2017年第5期11卷 703-713页

作者： Cao, Gang Lai, Edmund M. -K. Alam, Fakhrul Massey Univ Sch Engn & Adv Technol Auckland New Zealand Auckland Univ Technol Dept Informat Technol & Software Engn Auckland New Zealand

Model predictive control (MPC) of an unknown system that is modelled by Gaussian process (GP) techniques is studied. Using GP, the variances computed during the modelling and inference processes allow us to take model uncertainty into account. The main issue in using MPC to control systems modelled by GP is the propagation of such uncertainties within the control horizon. In this study, two approaches to solve this problem, called GPMPC1 and GPMPC2, are proposed. With GPMPC1, the original stochastic model predictive control (SMPC) problem is relaxed to a deterministic non-linear MPC based on a basic linearised GP local model. The resulting optimisation problem, though non-convex, can be solved by the sequential quadratic programming. By incorporating the model variance into the state vector, an extended local model is derived. This model allows us to relax the non-convex MPC problem to a convex one which can be solved by an active-set method efficiently. The performance of both approaches is demonstrated by applying them to two trajectory tracking problems. Results show that both GPMPC1 and GPMPC2 produce effective controls but GPMPC2 is much more efficient computationally.

关键词： nonlinear control systems Gaussian processes predictive control stochastic systems uncertain systems linear systems concave programming quadratic programming vectors trajectory control Gaussian process techniques unknown nonlinear systems inference processes model uncertainty GPMPC1 GPMPC2 stochastic model predictive control problem SMPC problem basic linearised GP local model nonconvex optimisation sequential quadratic programming state vector extended local model active-set method trajectory tracking problems

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Optimal operating conditions for the primary end of an integrated steel plant: Genetic adaptive search and classical techniques

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ISIJ INTERNATIONAL 1998年第1期38卷 98-105页

作者： Deo, B Deb, K Jha, S Sudhakar, V Sridhar, NV Indian Inst Technol Dept Mat & Met Engn Kanpur 208016 Uttar Pradesh India Indian Inst Technol Dept Mech Engn Kanpur 208016 Uttar Pradesh India

The optimal operating conditions for the primary end of an integrated steel plant, which essentially comprises of sintering plant, pelletizing plant, blast furnaces, oxygen steelmaking converters and electric are furnace, are found through a modern technique of optimization, namely, genetic adaptive search (GAS), and also through classical techniques of simplex search with simulated annealing (ASM) and sequential quadratic programming (SOP). A comparison of these techniques shows that GAS outperforms both the classical methods and obtains the lowest cost solution. Based on this study, it is recommended that GAS be used, in preference to other methods, in complex steel plant optimization problems.

关键词： steelmaking blast furnace optimization genetic algorithm simulated annealing sequential quadratic programming

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A tabu search based hybrid optimization approach for a fuzzy modelled unit commitment problem

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ELECTRIC POWER SYSTEMS RESEARCH 2006年第6-7期76卷 413-425页

作者： Victoire, TAA Jeyakumar, AE Karunyan Inst Technol Dept Elect & Elect Engn Coimbatore 641114 Tamil Nadu India Anna Univ Dept Elect & Elect Engn Coimbatore 641013 Tamil Nadu India

This article presents a solution model for the unit commitment problem (UCP) using fuzzy logic to address uncertainties in the problem. Hybrid tabu search (TS), particle swarm optimization (PSO) and sequential quadratic programming (SQP) technique (hybrid TS-PSO-SQP) is used to schedule the generating units based on the fuzzy logic decisions. The fitness function for the hybrid TS-PSO-SQP is formulated by combining the objective function of UCP and a penalty calculated from the fuzzy logic decisions. Fuzzy decisions are made based on the statistics of the load demand error and spinning reserve maintained at each hour. TS are used to solve the combinatorial sub-problem of the UCP. An improved random perturbation scheme and a simple method for generating initial feasible commitment schedule are proposed for the TS method. The non-linear programming sub-problem of the UCP is solved using the hybrid PSO-SQP technique. Simulation results on a practical Neyveli Thermal Power Station system (NTPS) in India and several example systems validate, the presented UCP model is reasonable by ensuring quality solution with sufficient level of spinning reserve throughout the scheduling horizon for secure operation of the system. (c) 2005 Elsevier B.V All rights reserved.

关键词： unit commitment fuzzy logic tabu search particle swarm optimization sequential quadratic programming

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DECOMPOSITION APPROACH TO SOLVING THE ALL-UP TRAJECTORY OPTIMIZATION PROBLEM

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JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 1992年第3期15卷 707-716页

作者： BELTRACCHI, TJ Aerospace Corporation Los Angeles California 90009

This paper describes a new approach to solving the all-up (ground to mission) trajectory optimization problem. The problem considered in this paper is that of placing a maximum payload into a specified orbit, where a booster is used to place the payload in a low Earth parking orbit from which an upper stage transfers the payload to its mission orbit. One approach to solving the all-up problem has been to combine the booster and upper stage into one large simulation/optimization problem;however, this results in a difficult problem that is computationally expensive to solve. The algorithm proposed in this paper does not require any all-up trajectories to be explicitly optimized, but simulates separately the booster and upper stage. The algorithm is based on solving the maximum throw weight to a park orbit (for the booster), maximum payload transfer from the park orbit to the mission orbit (for the upper stage), and a coordination problem that adjusts the park orbit parameters to find the all-up optimum (maximum) payload to the mission orbit.

关键词： Orbital Transfer Trajectory Optimization Earth Propellant Perigee Numerical Integration sequential quadratic programming Sensitivity Analysis Nonlinear programming Reaction Control System

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Numerical approximation of an SQP-type method for parameter identification

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SIAM JOURNAL ON NUMERICAL ANALYSIS 2002年第5期40卷 1775-1797页

作者： Burger, M Mühlhuber, W Johannes Kepler Univ Linz SFB Numer & Symbol Sci Comp F 013 A-4040 Linz Austria

This paper deals with the numerical approximation of the Levenberg-Marquardt SQP (LMSQP) method for parameter identification problems, which has been presented and analyzed in [M. Burger and W. Muhlhuber, Inverse Problems, 18 (2002), pp. 943-969]. It is shown that a Galerkin-type discretization leads to a convergent approximation and that the indefinite system arising from the Karush-Kuhn-Tucker (KKT) system is well-posed. In addition, we present a multilevel version of the Levenberg Marquardt method and discuss the simultaneous solution of the discretized KKT system by preconditioned iteration methods for indefinite problems. From a discussion of the numerical effort we conclude that these approaches may lead to a considerable speed-up with respect to standard iterative regularization methods that eliminate the underlying state equation. The numerical efficiency of the LMSQP method is confirmed by numerical examples.

关键词： parameter identification sequential quadratic programming iterative regularization Galerkin methods indefinite systems

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