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Learning strategies for adaptive equalizers using the constant modulus error criterion

INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING 1998年第2期12卷 97-116页

作者： Soni, RA Schirtzinger, TA Jenkins, WK Univ Illinois Dept Elect & Comp Engn Urbana IL 61801 USA Univ Illinois Coordinated Sci Lab Urbana IL 61801 USA

In communication systems, data are often corrupted during transmission due to imperfect communication channels. Since channel characteristics may be time varying or unknown prior to data transmission, adaptive equalizers are typically incorporated into the receiver to reduce the ill effects of the imperfect channel. Conventional equalizers utilize a training signal to achieve the proper correction. Blind equalization schemes do not require the use of a training signal, but instead attempt restoration based upon some known property of the transmitted signal. The constant modulus algorithm (CMA) is a blind equalization technique that may be used to equalize certain communication signals (e.g. BPSK, QPSK, and FM). This paper investigates three new techniques for adaptive equalization based on the constant modulus error criterion: (i) the fast quasi-Newton CMA, (ii) the conjugate gradient quasi-Newton CMA, (iii) the conjugate gradient CMA. These three methods are formulated analytically and evaluated experimentally on several typical communication channel models. (C) 1998 John Wiley & Sons, Ltd.

关键词： adaptive channel equalization blind equalization conjugate gradient algorithm constant modulus algorithm fast quasi-Newton CMA algorithm LMS algorithm

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Generalized eigenspace beamformer based on CG-Lanczos algorithm

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IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION 2003年第8期51卷 2146-2154页

作者： Segovia-Vargas, D Iñigo, F Sierra-Pérez, M Univ Carlos III Madrid Leganes 28911 Spain Univ Politecn Madrid E-28040 Madrid Spain

The generalized eigenspace beamformer (GEIB) has been presented as an efficient way to build a beamformer robust to calibration and pointing errors. Its main drawback is the great computational cost needed to develop the whole structure (mainly associated to obtaining the projection matrix). In this paper, the relation between conjugate gradient and Lanczos algorithm is used to modify the conjugate gradient algorithm [robust conjugate gradient method (RCGM)] so the eigenstructure of the correlation matrix can be obtained directly. The numerical problems associated to the loss of orthogonality in successive gradient vectors have also been overcome via selective orthogonalization (SO). In this way, the proposed RCGM is also robust to numerical problems. The overall computational cost in the construction of the GEIB has been reduced from O(N-3 + N-2) to O(3N(2)) by using the RCGM instead of a conventional projection algorithm. Computer simulations are also presented to demonstrate the merits of the algorithm.

关键词： conjugate gradient algorithm generalized eigenspace beamformer (GEIB) Lanczos algorithm

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Differential game approach to the mixed H-2-H-infinity problem

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JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 1997年第6期20卷 1229-1234页

作者： Sweriduk, GD Calise, AJ GEORGIA INST TECHNOL SCH AEROSP ENGN ATLANTA GA 30332 USA

H-infinity control theory has gained wide acceptance over the past 10 years as a valuable method of controller design. However, for most applications the solution results in a controller of higher dimension than that of the plant, As an alternative to controller order reduction, low-order controllers can be designed by fixing the order of the controller a priori. There also has been a growing interest in the mixed H-2-H-infinity problem. This problem has not been resolved completely, and there are few published solutions for the fixed-order case, A differential game formulation for the H-infinity problem is extended to address the mixed problem. The result is five first-order necessary conditions, and a conjugate gradient algorithm is used to search for solutions. To test the approach, a 10th-order problem describing the lateral dynamics of a high-performance helicopter in forward flight is used.

关键词： Observability Gramian conjugate gradient algorithm Helicopters Full State Feedback Model Order Reduction Riccati Equation Method Stability Derivatives White Noise Sideslip Optimal Projection Equations

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

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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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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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NEWTON-LIKE MINIMAL RESIDUAL METHODS APPLIED TO TRANSONIC FLOW CALCULATIONS

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AIAA JOURNAL 1985年第4期23卷 515-521页

作者： WONG, YS NASA LANGLEY RES CTRINST COMP APPL SCI & ENGNHAMPTONVA 23665 MCGILL UNIV MONTREAL H3A 2T5QUEBECCANADA

A new computational technique for the solution of the full potential equation is presented. The method consists of outer and inner iterations. The outer iteration is based on a Newton-like algorithm;a preconditioned minimal residual method is used to seek an approximate solution of the system of linear equations arising at each inner iteration. The present iterative scheme is formulated so that the uncertainties and difficulties associated with many iterative techniques are eliminated–namely, the requirements of acceleration parameters and the treatment of additional boundary conditions for the intermediate variables. Numerical experiments based on the new method for transonic potential flows around an NACA 0012 airfoil at different Mach numbers and different angles of attack are presented and the results are compared with those obtained by the approximate factorization technique. Extension of the present method to three-dimensional flow calculations and its application in finite element methods for fluid dynamics problems are also discussed. © 1985 American Institute of Aeronautics and Astronautics, Inc., All rights reserved.

关键词： Supersonic Flow NACA 0012 Angle of Attack Finite Element Formulation Pressure Coefficient Mach Numbers Fluid Density conjugate gradient algorithm NASA Langley Research Center Finite Difference Method

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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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Cost optimization of reinforced high strength concrete T-sections in flexure

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STRUCTURAL ENGINEERING AND MECHANICS 2014年第1期49卷 65-80页

作者： Tiliouine, B. Fedghouche, F. Ecole Natl Polytech Dept Genie Civil Lab Genie Sism & Dynam Struct Algiers 16200 Algeria

This paper reports on the development of a minimum cost design model and its application for obtaining economic designs for reinforced High Strength Concrete (HSC) T-sections in bending under ultimate limit state conditions. Cost objective functions, behavior constraint including material nonlinearities of steel and HSC, conditions on strain compatibility in steel and concrete and geometric design variable constraints are derived and implemented within the conjugate gradient optimization algorithm. Particular attention is paid to problem formulation, solution behavior and economic considerations. A typical example problem is considered to illustrate the applicability of the minimum cost design model and solution methodology. Results are confronted to design solutions derived from conventional design office methods to evaluate the performance of the cost model and its sensitivity to a wide range of unit cost ratios of construction materials and various classes of HSC described in Eurocode2. It is shown, among others that optimal solutions achieved using the present approach can lead to substantial savings in the amount of construction materials to be used. In addition, the proposed approach is practically simple, reliable and computationally effective compared to standard design procedures used in current engineering practice.

关键词： cost optimization high strength concrete (HSC) T-sections ultimate limit state (ULS) eurocode2 (EC-2) nonlinear programming conjugate gradient algorithm

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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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