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A Photonics-Assisted Wideband Radar Jamming Signal Generator With Flexible Tunability and multidimensional optimization

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IEEE TRANSACTIONS ON MICROWAVE THEORY AND TECHNIQUES 2023年第12期71卷 5367-5380页

作者： Men, Yuan Wen, Aijun Yang, Fuxian Chen, Changchang Xidian Univ Collaborat Innovat Ctr Informat Sensing & Understa State Key Lab Integrated Serv Networks Xian 710071 Peoples R China

In this article, a wideband radar jamming signal generator with flexible tunability and multidimensional optimization is proposed based on a dual-polarization Mach-Zehnder modulator (DPol-MZM) cascaded with a phase modulator (PM). Interrupted-sampling jamming is achieved with a sampling frequency of 1 MHz and duty cycles of 50% and 25%. Cross-cosinusoidal phase-modulated jamming with frequencies of 2 and 4 MHz is also built. A compound jamming signal that combines interrupted-sampling jamming and cross-cosinusoidal phase-modulated jamming is generated. The jamming effectiveness of the above schemes is evaluated, and multiple false targets are obtained after cross correlation with the original radar signal. The number of generated false targets can reach 12. The jamming mechanism of the proposed scheme is discussed, and the jamming effectiveness in radar imaging is also analyzed. The proposed scheme has a stable, compact, and efficient structure and meets the requirements of the electronic warfare system, which will get potential applications in future electronic warfare systems.

关键词： Flexible tunability microwave photonics multidimensional optimization pulse compression signal wideband radar jamming signal generation

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Review of multidimensional optimization Methods

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JOURNAL OF COMMUNICATIONS TECHNOLOGY AND ELECTRONICS 2015年第6期60卷 625-636页

作者： Zakharova, E. M. Minashina, I. K. State Univ Moscow Inst Phys & Technol Dolgoprudnyi 141700 Moscow Oblast Russia

Basic multidimensional optimization methods are considered and compared by their efficiency. The applicability of the considered algorithms to different types of optimized functions is analyzed.

关键词： multidimensional optimization constrained optimization local extremum global extremum multiextremal objective function

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Global best-guided oppositional algorithm for solving multidimensional optimization problems

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ENGINEERING WITH COMPUTERS 2020年第1期36卷 43-73页

作者： Turgut, Mert Sinan Turgut, Oguz Emrah Ege Univ Fac Engn Dept Mech Engn TR-35040 Izmir Turkey Bakircay Univ Fac Engn Dept Mech Engn Izmir Turkey

This paper presents an alternative optimization algorithm to the literature optimizers by introducing global best-guided oppositional-based learning method. The procedure at hand uses the active and recent manipulation schemes of oppositional learning procedure by applying some modifications to them. The first part of the algorithm deals with searching the optimum solution around the current best solution by means of the ensemble learning-based strategy through which unfeasible and semi-optimum solutions have been straightforwardly eliminated. The second part of the algorithm benefits the useful merits of the quasi-oppositional learning strategy to not only improve the solution diversity but also enhance the convergence speed of the whole algorithm. A set of 22 optimization benchmark functions have been solved and corresponding results have been compared with the outcomes of the well-known literature optimization algorithms. Then, a bunch of parameter estimation problem consisting of hard-to-solve real world applications has been analyzed by the proposed method. Following that, eight widely applied constrained benchmark problems along with well-designed 12 constrained test cases proposed in CEC 2006 session have been solved and evaluated in terms of statistical analysis. Finally, a heat exchanger design problem taken from literature study has been solved through the proposed algorithm and respective solutions have been benchmarked against the prevalent optimization algorithms. Comparison results show that optimization procedure dealt with in this study is capable of achieving the utmost performance in solving multidimensional optimization algorithms.

关键词： Heat exchanger design multidimensional optimization Oppositional-based learning Parameter estimation Stochastic search

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ASPECTS OF multidimensional optimization

ASPECTS OF MULTIDIMENSIONAL OPTIMIZATION

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23rd International DAAAM Symposium on Intelligent Manufacturing and Automation - Focus on Sustainability

作者： Mateia, Nicolae Adrian Cechin-Crista, Persida

ISBN: (纸本)9783901509919

The complexity and dynamics of present-day problems lead to a new stage in research and in seeking the optimal solutions in technical, economic and social environments. Therefore, advanced multidimensional optimization techniques are based not only on mathematic concepts, but also on elements of artificial intelligence. In this paper we present a modified version of the Nelder & Mead algorithm, as well as the trends of Genetic Algorithms and Swarm Intelligence.

关键词： multidimensional optimization Nelder & Mead algorithm (simplex) evolutionary algorithms hybrid algorithm

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Region Identification based multidimensional optimization of the Performance of Semiconductor Laser Amplifier

Region Identification based multidimensional optimization of...

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IEEE Photonics Conference (PHO)

作者： Islam, Ehtesanul Awal, Md. Rabiul Ahsanullah Univ Sci & Technol Dept Elect & Elect Engn Dhaka Bangladesh Stanford Univ Dept Elect & Elect Engn Stanford CA 94305 USA

We have investigated a region identification technique for multi-dimensional adaptive control in an optical communication system. Performance of SLA is evaluated in terms of gain, power, bandwidth, extinction ratio.

ISBN: (纸本)9781424489398

关键词： Bit error rate Extinction ratio Gain Optical amplifiers Optical receivers Optical sensors Stimulated emission adaptive control multidimensional adaptive control multidimensional optimization optical communication equipment optical communication system reg

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A Branch and Bound algorithm for multidimensional Holder optimization: Estimation of the age-dependent viral hepatitis A infection force

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MATHEMATICS AND COMPUTERS IN SIMULATION 2024年 217卷 311-326页

作者： Naffeti, Bechir Ammar, Hamadi Ben Aribi, Walid Univ Tunis El Manar Pasteur Inst Tunis Lab BioInformat Biomath & Biostat Tunis Tunisia Carthage Univ Natl Sch Adv Sci & Technol BorjCedria LR16ES08 LR16ES08 Tunis Tunisia Carthage Univ Fac Econ & Management Nabeul Nabeul Tunisia Esprit Sch Business Sch Business Tunis 2083 Tunisia

In the present framework, we develop a branch and bound multidimensional Holder optimiza-tion method. This method is composed of two subroutines. The first one allows converting the multivariate objective function into a single variable one using the alpha-dense space fitting curves. In the second subroutine, we minimize the single variable function resulting from the first subroutine. To achieve this task, we develop a novel iterative optimization method reducing the feasible region in each iteration taking into account the property of Holder of the objective function. We apply this method to solve a parameters identification problem resulting from the modelling of the spread of viral hepatitis A in the central west of Tunisia following the increase in the number of infections. The Tunisian health authorities wanted to know the prevalence and the infection force of the virus in order to identify the situation and take the necessary measures. The modelling leads to a minimization problem of the least square multivariate error between the observed values and the theoretical ones. Besides, we implement the proposed method in some numerical experiments to evaluate its efficiency.

关键词： multidimensional optimization Holder functions Branch and bound methods Alpha dense space fitting curves Modelling of the viral hepatitis A SEIRD model Seroprevalence Infection force Linear predictor Logit link

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Robustness of multidimensional optimization outcomes: a general approach and a case study 20

Robustness of multidimensional optimization outcomes: a gene...

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Proceedings of the 2020 Spring Simulation Conference

作者： Negin Forouzesh Layne T. Watson Alexey V. Onufriev Virginia Polytechnic Institute and State University

ISBN: (纸本)9781713812883

In multidimensional parameter optimization of complex systems, the preferred solution must also be robust to virtually inevitable perturbations and uncertainties. Having a conceptually simple and computationally facile metric that can help distinguish between candidate optimum solutions in a post processing step is useful. Motivated by free energy function in statistical physics, which evaluates a trade-off between entropy and enthalpy, here we introduce a novel statistical robustness metric that assesses robustness with respect to possible to inevitable uncertainties in the objective function values or optimal parameters. The metric is the expected value 〈E〉 of the objective function, evaluated using weighted samples in a box around each optimum. The width of the sample distribution is problem-specific. As a case study, the proposed robustness metric is employed to find the most robust optimal solution in 5-dimensional parameter space in the context of dielectric boundary optimization in atomistic modeling, relevant to computational drug discovery.

关键词： computational drug discovery robustness metric multidimensional optimization

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Numerical solution for high order differential equations using a hybrid neural network - optimization method

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APPLIED MATHEMATICS AND COMPUTATION 2006年第1期183卷 260-271页

作者： Malek, A. Beidokhti, R. Shekari Tarbiat Modares Univ Dept Math Tehran Iran

This paper reports a novel hybrid method based on optimization techniques and neural networks methods for the solution of high order ordinary differential equations. Here neural networks is considered as a part of large field called neural computing or soft computing. This means that we propose a new solution method for the approximated solution of high order ordinary differential equations using innovative mathematical tools and neural-like systems of computation. This hybrid method can result in improved numerical methods for solving initial/boundary value problems, without using preassigned discretisation points. The mixture of feed forward neural networks and optimization techniques, based on Nelder-Mead method is used to introduce the close analytic form of the solution for the differential equation. Excellent test results are obtained for the solution of lower and higher order differential equations. The model finds approximation solution for the differential equation inside and outside the domain of consideration for the close enough neighborhood of initial/boundary points. Numerical examples are described to demonstrate the method. (c) 2006 Elsevier Inc. All rights reserved.

关键词： ordinary differential equations feed forward artificial neural networks multidimensional optimization Nelder-Mead method

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Solving initial-boundary value problems for systems of partial differential equations using neural networks and optimization techniques

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JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS 2009年第9期346卷 898-913页

作者： Beidokhti, R. Shekari Malek, A. Tarbiat Modares Univ Dept Math Fac Basic Sci Tehran Iran

A general system of the time-dependent partial differential equations containing several arbitrary initial and boundary conditions is considered. A hybrid method based on artificial neural networks, minimization techniques and collocation methods is proposed to determine a related approximate solution in a closed analytical form. The optimal values for the corresponding adjustable parameters are calculated. An accurate approximate solution is obtained, that works well for interior and exterior points of the original domain. Numerical efficiency and accuracy of the hybrid method are investigated by two-test problems including an initial value and a boundary value problem for the two-dimensional biharmonic equation. (C) 2009 Published by Elsevier Ltd. on behalf of The Franklin Institute.

关键词： Time-dependent system of partial differential equations Initial-boundary value problems Feed forward artificial neural networks multidimensional optimization

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Chameleon Swarm Algorithm Assisted optimization of U-Slot Patch Antenna for Quad-Band Applications

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IEEE ACCESS 2022年 10卷 74152-74163页

作者： Uluslu, Ahmet Istanbul Univ Cerrahpasa Dept Elect & Automat TR-34500 Istanbul Turkey

Existing optimization algorithms are insufficient in the face of problems with difficult measurement functions as well as a large number of design parameters. Therefore, to achieve such challenging applications, the Chameleon swarm algorithm and its variants, which belong to the family of meta-heuristic algorithms that have not been used in any antenna optimization study before, are used together with 3 different objective functions fitted from mathematical models. Then, a U-slot antenna application with 12 different variable design parameters with resonance frequencies of 3.5 GHz, 3.7 GHz, 5.2 GHz and 5.8 GHz is considered as a multidimensional and single-objective optimization problem. In this study, first of all, the success of the algorithm is reinforced by comparing the performance with other commonly used single and multi-objective optimization algorithms. In addition, the results obtained with different population parameters, weight coefficients, objective functions and variant models were compared. All these processes are compared within themselves, and the antenna results of the most successful result are displayed as 3D electromagnetic simulation. The results show that the optimization processes proposed for an antenna designer are a safe, practical and efficient solution for multidimensional and single-objective antenna optimization applications. In addition, any optimization problem with a large number of variable design parameters can undoubtedly be adapted to the Chameleon swarm algorithm.

关键词： optimization Antennas Microstrip antennas Heuristic algorithms Mathematical models Substrates Patch antennas Quad-band eCSA patch antenna multidimensional optimization optimization techniques chameleon swarm algorithm evolutionary algorithms swarm intelligence algorithms

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