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On the generalized low rank approximation of the correlation matrices arising in the asset portfolio

LINEAR ALGEBRA AND ITS APPLICATIONS 2014年 461卷 1-17页

作者： Duan, Xuefeng Bai, Jianchao Zhang, Maojun Zhang, Xinjun Guilin Univ Elect Technol Coll Math & Computat Sci Guilin 541004 Peoples R China

In this paper, we consider the generalized low rank approximation of the correlation matrices problem which arises in the asset portfolio. We first characterize the feasible set by using the Gramian representation together with a special trigonometric function transform, and then transform the generalized low rank approximation of the correlation matrices problem into an unconstrained optimization problem. Finally, we use the conjugate gradient algorithm with the strong Wolfe line search to solve the unconstrained optimization problem. Numerical examples show that our new method is feasible and effective. Published by Elsevier Inc.

关键词： Generalized low rank approximation Correlation matrix Asset portfolio Feasible set conjugate gradient algorithm

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A parallel algorithm for pulsed laser infrared tomography

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PATTERN RECOGNITION LETTERS 1998年第5-6期19卷 521-526页

作者： Shoari, S Bagherzadeh, N Goodman, D Milner, TE Smithies, DJ Nelson, JS Univ Calif Irvine Dept Elect & Comp Engn Irvine CA 92697 USA Univ Calif Lawrence Livermore Natl Lab Livermore CA 94550 USA Univ Calif Irvine Beckman Laser Inst & Med Clin Irvine CA 92697 USA

A three-dimensional Tomographic Reconstruction algorithm (3D TRA) has been developed and implemented on a parallel machine at the Maul High Performance Computing Center (MHPCC). Given a time sequence of infrared images, this algorithm provides information to determine the location, physical dimensions, and initial space-dependent temperature increase of laser heated blood vessels. In this approach the serial computation of a 3D TRA is decomposed into multiple tasks which can be executed in parallel to reduce the computation time. (C) 1998 Elsevier Science B.V. All rights reserved.

关键词： parallel processing system performance conjugate gradient algorithm infrared image

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Neural network approach for nonlinear aeroelastic analysis

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JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2003年第1期26卷 99-105页

作者： Voitcu, O Wong, YS Univ Alberta Dept Math & Stat Sci Edmonton AB T6G 2G1 Canada

A new approach is proposed, based on the use of artificial neural networks, for predicting nonlinear aeroelastic oscillations. Our objective is to reconstruct the asymptotic state of the nonlinear behavior of an aeroelastic model when only a limited segment of the transient data is known. An original neural network architecture is proposed and is used to predict the nonlinear motions of an aeroelastic system modeling a self-excited two-degree-of-freedom airfoil oscillating in pitch and plunge. When a segment of the transient state of the given signal is used for training, the neural network is capable of correctly predicting the corresponding limit-cycle oscillations, damped oscillations, or unstable divergent oscillations. The network training set consists of numerically generated data or data obtained from a wind-tunnel experiment. A neural network used in conjunction with a wavelet decomposition is presented, which proves to be capable of extracting the values of the damping coefficients and frequencies from the predicted signal. Neural networks, thus, prove to be useful tools in nonlinear aeroelastic analysis.

关键词： Neural Networks Aeroelastic Response Prediction Control Stability conjugate gradient algorithm Structural Nonlinearities Nonlinear Dynamics conjugate gradient Method Numerical Simulation Structural Failure Mathematical Models

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COMPUTATIONAL METHOD FOR MINIMAX OPTIMIZATION IN THE TIME-DOMAIN

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JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 1994年第3期17卷 473-479页

作者： OHTSUKA, T FUJII, HA Tokyo Metropolitan Institute of Technology Tokyo 191 Japan

A computational method is proposed in this paper for a minimax problem in the time domain. A minimax problem for a linear system is formulated and analyzed by vector space methods. The uniqueness of the optimal solution is analyzed readily in the present approach, and its geometric interpretation is presented. The further analysis based on duality shows that the infimum or a lower bound of the value of the performance index is calculated through supplementary optimization problems, and the minimax problem is solved as an optimization problem with an inequality constraint. The proposed approach provides a simple computational method for the minimax problem. Two numerical examples demonstrate simple implementation of the proposed method.

关键词： Computational Methods Trajectory Optimization Transient Response Terminal Control Attitude Control Bending Moment conjugate gradient algorithm Aerospace Engineering Numerical Simulation Nonlinear Systems

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Image-Based Deep Reinforcement Meta-Learning for Autonomous Lunar Landing

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JOURNAL OF SPACECRAFT AND ROCKETS 2022年第1期59卷 153-165页

作者： Scorsoglio, Andrea D'Ambrosio, Andrea Ghilardi, Luca Gaudet, Brian Curti, Fabio Furfaro, Roberto Univ Arizona Dept Syst & Ind Engn 1127 E James E Rogers Way Tucson AZ 85721 USA Sapienza Univ Rome Sch Aerosp Engn Via Salaria 851 I-00138 Rome Italy Univ Arizona Dept Aerosp & Mech Engn 1127 E James E Rogers Way Tucson AZ 85721 USA

Future exploration and human missions on large planetary bodies (e.g., moon, Mars) will require advanced guidance navigation and control algorithms for the powered descent phase, which must be capable of unprecedented levels of autonomy. The advent of machine learning, and specifically reinforcement learning, has enabled new possibilities for closed-loop autonomous guidance and navigation. In this paper, image-based reinforcement metalearning is applied to solve the lunar pinpoint powered descent and landing task with uncertain dynamic parameters and actuator failure. The agent, a deep neural network, takes real-time images and ranging observations acquired during the descent and maps them directly to thrust command (i.e., sensor-to-action policy). Training and validation of the algorithm and Monte Carlo simulations shows that the resulting closed-loop guidance policy reaches errors in the order of meters in different scenarios, even when the environment is partially observed, and the state of the spacecraft is not fully known.

关键词： Lunar Landing Deep Convolutional Neural Network Environment Sensors Closed Loop System Reinforcement Learning conjugate gradient algorithm Moon Actuators Monte Carlo Simulation Trajectory Optimization

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Controller Application of a Multi-Layer Quantum Neural Network with Qubit Neurons

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JOURNAL OF ADVANCED MECHANICAL DESIGN SYSTEMS AND MANUFACTURING 2012年第4期6卷 526-540页

作者： Takahashi, Kazuhiko Kurokawa, Motoki Hashimoto, Masafumi Doshisha Univ Dept Informat Syst Design Kyotanabe Kyoto 6100321 Japan Doshisha Univ Grad Sch Kyotanabe Kyoto 6100321 Japan Doshisha Univ Dept Intelligent Informat Engn & Sci Kyotanabe Kyoto 6100321 Japan

This paper investigates a quantum neural network and discusses its application in control systems. A learning-type neural network-based controller that uses a multi-layer quantum neural network having qubit neurons as its information processing unit is proposed. Three learning algorithms;a back-propagation algorithm, a conjugate gradient algorithm and a real-coded genetic algorithm, are investigated to supervise the training of the multi-layer quantum neural network. To evaluate the learning performance and the capability of the quantum neural network-based controller, we conducted computational experiments for controlling a nonlinear discrete-time plant and a nonholonomic system - in this study a two-wheeled robot. The results of computational experiments confirm both the feasibility and the effectiveness of the quantum neural network-based controller and that the real-coded genetic algorithm is suitable for the learning method of the quantum neural network-based controller.

关键词： Quantum Neural Network Qubit Neuron Back-Propagation algorithm conjugate gradient algorithm Real-Coded Genetic algorithm Direct Controller Nonholonomic Robot

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Internal surfaces including phase change materials for passive optimal shift of solar heat gain

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INTERNATIONAL JOURNAL OF THERMAL SCIENCES 2010年第11期49卷 2148-2156页

作者： Arnault, Axel Mathieu-Potvin, Francois Gosselin, Louis Univ Laval Dept Genie Mecan Quebec City PQ G1V 0A6 Canada

The thermal inertia of building internal components may be used to shift the irradiation (solar) heat load, which could result in substantial energy saving. In this paper, a numerical model was implemented to determine the thermal performance of internal surfaces including Phase Change Materials (PCM). The model was exploited to compare a typical concrete floor with a floor with PCM. Thermal performance was defined by three different objective functions, each pinpointing different thermal characteristics of the heat load. First, parametric studies were performed to understand the influence of the thickness of a typical concrete floor. Then, the optimization of the melting temperature, thickness and position of a PCM layer included in a floor was performed. These analyses used either simplified or real weather conditions (for Quebec City). Results showed that the thickness of the concrete floor could be optimized based on the three criteria retained. Also, the floor performance may be enhanced by the inclusion of a PCM layer. It was shown that the gain of performance brought by the internal surfaces thermal mass strongly depends on the weather conditions considered. This paper provides a fundamental understanding of PCM influence on internal surfaces. (C) 2010 Elsevier Masson SAS. All rights reserved.

关键词： Floor conjugate gradient algorithm Solar heat gain Phase change material HVAC Optimization

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Inverse space-dependent source problem for a time-fractional diffusion equation by an adjoint problem approach

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JOURNAL OF INVERSE AND ILL-POSED PROBLEMS 2019年第1期27卷 1-16页

作者： Yan, Xiong Bin Wei, Ting Lanzhou Univ Sch Math & Stat Lanzhou 730030 Gansu Peoples R China

In this paper, we consider an inverse space-dependent source problem for a time-fractional diffusion equation by an adjoint problem approach;that is, to determine the space-dependent source term from a noisy final data. Based on the series expression of the solution for the direct problem, we improve the regularity of the weak solution for the direct problem under strong conditions, and we provide the existence and uniqueness for the adjoint problem. Further, we use the Tikhonov regularization method to solve the inverse source problem and provide a conjugate gradient algorithm to find an approximation to the minimizer of the Tikhonov regularization functional. Numerical examples in one-dimensional and two-dimensional cases are provided to show the effectiveness of the proposed method.

关键词： Inverse spatial source problem Tikhonov regularization uniqueness conjugate gradient algorithm

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Signal Sorting algorithm of Hybrid Frequency Hopping Network Station Based on Neural Network

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IEEE ACCESS 2021年 9卷 35924-35931页

作者： Wang, Zhongyong Zhang, Beibei Zhu, Zhengyu Wang, Zixuan Gong, Kexian Zhengzhou Univ Sch Informat Engn Zhengzhou 450001 Peoples R China Ningxia Univ Ningxia Key Lab Photovolta Mat Yinchuan 750021 Ningxia Peoples R China

In non-cooperative frequency hopping communication system, the frequency hopping network station sorting of the received hybrid signals plays an important role and becomes an active research area in recent years. In order to solve the problem that the currently widely used clustering algorithm cannot achieve satisfactory accuracy. In this paper, we propose a signal sorting method for hybrid frequency hopping network stations by applying the neural network to classify the frequency hopping description words of signals. Additionally, the conjugate gradient algorithm is utilized in the neural network training process to improve the convergence speed. Once the neural network training is finished, only one frequency hopping description word of the input signal is required to obtain its own network station label in real time. Simulation results demonstrate that when compared with the clustering algorithm, the proposed algorithm converges with less iterations and delivers better sorting accuracy, especially in a low signal to noise ratio environment.

关键词： Sorting Time-frequency analysis Clustering algorithms Neural networks Spread spectrum communication Frequency estimation Feature extraction Frequency hopping communication signal sorting neural network Kmeans clustering conjugate gradient algorithm

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A Bi-Level Consensus ADMM-Based Fully Distributed Inverter-Based Volt/Var Control Method for Active Distribution Networks

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IEEE TRANSACTIONS ON POWER SYSTEMS 2022年第1期37卷 476-487页

作者： Ju, Yuntao Zhang, Zifeng Wu, Wenchuan Liu, Wenwu Zhang, Ruosi China Agr Univ Coll Informat & Elect Engn Beijing 100083 Peoples R China Tsinghua Univ Dept Elect Engn State Key Lab Power Syst Beijing 100084 Peoples R China China Agr Univ Informat & Elect Engn Coll Beijing 100083 Peoples R China

The distributed Volt/Var control solution is a promising one for active distribution networks (ADNs), but a critical issue is how to improve the distributed algorithm's convergence with less communication burden. In this paper, we propose a bi-level consensus alternation direction multiplier method-based fully distributed Volt/Var optimization algorithm (B-FADMM) for ADNs by exploiting inverter-based rapid control devices. In the first level, the Volt/Var optimal solution of subpartitions is obtained in parallel, and the second level completes the variable increment of each partition to correct the search direction of the first level. In the algorithm, firstly, an adaptive matrix omega initialization strategy is proposed to adapt to the different magnitude of variables, which avoids non-convergence or slow convergence of the algorithm. Secondly, the null-space method is used in the second level of the algorithm to obtain the null-space basis matrix on the active constraint of the subpartition, which reduces the dimensions of the problem and the computational complexity. In addition, the conjugate gradient (CG) algorithm is used to update the dual multiplier of each partition only by exchanging coupling information among partitions, which protects information privacy. Experiments showed the proposed approach attains the same result as the centralized, the computational performance of the proposed method is far superior to some popular other distributed methods in terms of accuracy, computational efficiency, and scalability.

关键词： Consensus ADMM distribution reactive power optimization conjugate gradient algorithm active distribution networks

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