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检索条件"任意字段=Conference on High Performance Algorithms and Software for Nonlinear Optimization"

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nonlinear Structural Finite Element Model Updating Using Batch Bayesian Estimation

Nonlinear Structural Finite Element Model Updating Using Bat...

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Model Validation and Uncertainty Quantification, Volume 3

作者： Ebrahimian, Hamed Astroza, Rodrigo Conte, Joel P. Univ Calif San Diego 9500 Gilman Dr La Jolla CA 92093 USA Univ Los Andes Santiago 12455 Chile

ISBN: (纸本)9783319152240

This paper proposes framework for nonlinear finite element (FE) model updating, in which state-of-the-art nonlinear structural FE modeling and analysis techniques are combined with the maximum likelihood estimation (MLE) method to estimate time-invariant parameters governing the nonlinear hysteretic material constitutive models used in the FE model of the structure. Using the MLE as a parameter estimation tool results in a nonlinear optimization problem, which can be efficiently solved using gradient-based optimization algorithms such as the interior-point method. Gradient-based optimization algorithms require the FE response sensitivities with respect to the material parameters to be identified, which are computed accurately and efficiently using the direct differentiation method (DDM). The estimation uncertainties are evaluated based on the Cramer-Rao lower bound (CRLB) theorem by computing the exact Fisher Information matrix using the FE response sensitivities. A proof-of-concept example, consisting of a cantilever steel column representing a bridge pier, is provided to validate the proposed nonlinear FE model updating framework. The simulated responses of this bridge pier to an earthquake ground motion is polluted with artificial output measurement noise and used to estimate the unknown parameters of the material constitutive model. The example illustrates the excellent performance of the proposed parameter estimation framework even in the presence of high measurement noise.

关键词： nonlinear finite element model Model updating Bayesian estimation nonlinear system identification Damage identification

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Stochastic optimization of a neural network-based controller for aggressive maneuvers on loose surfaces

Stochastic optimization of a neural network-based controller...

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IEEE/RSJ International conference on Intelligent Robots and Systems

作者： Terekhov, Alexander V. Mouret, Jean-Baptiste Grand, Christophe UPMC CNRS Inst Syst Intelligents & Robot F-75005 Paris France

ISBN: (纸本)9781424466757

In this study we develop a feedback controller for a four wheeled autonomous mobile robot. The purpose of the controller is to guarantee robust performance of an aggressive maneuver (90 degrees turn) at high velocity (about 10 m/s) on a loose surface (dirty road). To tackle this highly non-linear control problem, we employ multi-objective evolutionary algorithms to explore and optimize the parameters of a neural network-based controller. The obtained controller is shown to be robust with respect to uncertainties of the robot parameters, speed of the maneuver and properties of the ground. The controller is tested using two mathematical models of significantly different complexity and accuracy.

关键词： aggressive maneuvers closed loop systems feedback feedback controller mobile robots multi-objective evolutionary algorithms neural network based controller neurocontrollers nonlinear control nonlinear control systems optimisation stochastic optimization stochastic processes wheeled autonomous mobile robot

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Shape optimization with nonlinear Conjugate Gradient Methods 13th

Shape Optimization with Nonlinear Conjugate Gradient Methods

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13th International conference on Spectral and high Order Methods, ICOSAHOM 2021

作者： Blauth, Sebastian Fraunhofer ITWM Kaiserslautern Germany

ISBN: (纸本)9783031204319

In this chapter, we investigate recently proposed nonlinear conjugate gradient (NCG) methods for shape optimization problems. We briefly introduce the methods as well as the corresponding theoretical background and investigate their performance numerically. The obtained results confirm that the NCG methods are efficient and attractive solution algorithms for shape optimization problems. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Shape optimization

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Improving Generalization performance of Adaptive Learning Rate by Switching from Block Diagonal Matrix Preconditioning to SGD

Improving Generalization Performance of Adaptive Learning Ra...

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International Joint conference on Neural Networks (IJCNN) held as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Ida, Yasutoshi Fujiwara, Yasuhiro NE Software Innovat Ctr Tokyo Japan NTT Commun Sci Labs Tokyo Japan

ISBN: (纸本)9781728169262

Deep Neural Networks (DNNs) are widely used for various applications. Although adaptive learning rate algorithms are attractive for DNN training, their theoretical performance remains unclear. In fact, published analyses consider only simple optimization settings such as convex optimization, none of which are applicable to DNN training. This paper proposes TSO-ALRA, a two-stage optimizer using an adaptive learning rate algorithm;it is based on a full analysis of two approaches that do suit DNNs: parameter updates along geodesics on the statistical manifold and covariance structure of gradients. Our analysis reveals that the diagonal approximation used by existing adaptive learning rate algorithms inevitably degrades their efficiency. In addition, our analysis suggests that adaptive learning rate algorithms suffer drops in generalization performance in the last phase of training. To overcome these problems, TSO-ALRA combines an effective approximation technique and a switching strategy. Our experiments on several models and datasets show that TSO-ALRA efficiently converges with high generalization performance.

关键词： Deep neural networks

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Design and optimization, steady-state and dynamic analysis of synchronous reluctance motors controlled by voltage-fed converters with nonlinear controllers

Design and optimization, steady-state and dynamic analysis o...

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IEEE International Electric Machines and Drives conference (IEMDC 99)

作者： Lyshevski, SE Nazarov, A El-Antably, A Yokomoto, C Sinha, ASC Rizkalla, M El-Sharkawy, M Purdue Univ Dept Elect Engn Indianapolis IN 46202 USA

ISBN: (纸本)0780352939

This paper presents innovative results to improve the design and manufacture of high-performance synchronous reluctance machines. These results have been obtained from our research in analyzing and synthesizing advanced control algorithms to promote the competitiveness of three-phase synchronous reluctance machines in electric drives in comparison with permanent-magnet synchronous motors and induction machines. These results have direct application in the design and manufacture of electric- and hybrid-electric drivetrains for light-, medium-, and heavy-duty vehicles. First, we will report on the dynamic optimization of medium-duty synchronous reluctance machines described by nonlinear differential equations. Second, we describe a new design optimization method, based upon nonlinear electromagnetic analysis, to improve steady-state performance and to enhance the operating envelope. highly efficient, high-speed synchronous reluctance motors, ranging from 10 kW to 100 kW, were manufactured and tested. The design methods ensure cost-effective production of a new generation of state-of-the-art synchronous reluctance motors. To meet specified levels of performance, advanced control laws are needed, and highly detailed dynamical nonlinear analysis must be performed in addition to the steady-state optimization. This paper develops a nonlinear model of synchronous reluctance motors that incorporates saturation effects. Kirchhoff's and Newton's laws are used to derive the models. The application of Park's transformation results in a set of differential equations in the rotor reference frame;that is, the quadrature-, direct-, and zero-axis voltage and current quantities are used in analysis, modeling and design. Robust controllers are developed to guarantee closed-loop system stability and attain the disturbance rejection.

关键词： Electric drives

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Robust nonlinear acoustic echo cancellation using a metaheuristic optimization approach

Robust nonlinear acoustic echo cancellation using a metaheur...

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IEEE International conference on Digital Signal Processing, DSP 2015

作者： Merabti, Hocine Massicotte, Daniel Department of Electrical and Computer Engineering Laboratoire des Signaux et Systèmes Intégrés Université du Québec À Trois-RivièresQC Canada

ISBN: (纸本)9781479980581

nonlinearities in audio systems are caused by different nonlinear components like amplifiers and speakers. The nature of these impairments makes acoustic echo cancellation (AEC) harder to achieve, requiring the use of advanced and complex algorithms to offer satisfying performance. Adaptive filters in combination with nonlinear mapping schemes like Volterra and Hammerstein are widely used for this purpose. In this work, we address the AEC problem in the presence of severe nonlinear distortion. The proposed method based on metaheuristic optimization uses a genetic algorithm (GA) to estimate the nonlinear function parameters and the room impulse response. The method is compared to a reference technique that uses a sigmoid transform approach in conjunction with the recursive least square (RLS) algorithm. Simulation results show high robustness of the proposed approach to strong nonlinearities and saturation effects compared to the reference method. © 2015 IEEE.

关键词： Genetic algorithms

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Optimal tuning strategy for predictive controllers 7

Optimal tuning strategy for predictive controllers

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7th International conference on Modelling, Identification and Control (ICMIC)

作者： Adel, Taeib Abdelkader, Chaari Univ Tunis Natl High Sch Engn Tunis ENSIT Dept Engn Res Unit Control Monitoring & Safety Syst Tunis Tunisia

ISBN: (纸本)9780956715753

This paper describes the development of a method to optimally tune constrained MPC algorithms for a nonlinear process. The T-S model is firstly established for nonlinear systems and its sequence parameters of fuzzy rules are identified by local recursive least square method. The proposed method is obtained by minimizing performance criteria in the worst-case conditions to control the process system, thus assuring robustness to the set of optimum tuning parameters. The resulting constrained mixed-integer nonlinear optimization problem is solved on the basis of a version of the particle swarm optimization technique. The practicality and effectiveness of the identification and control scheme is demonstrated by simulation results.

关键词： MPC PSO Takagi-Sugeno Fuzzy logic

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Distributed Parameter optimization Scheduling Strategy and System Design Based on Mesos 5

Distributed Parameter Optimization Scheduling Strategy and S...

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5th IEEE Int Conf on Big Data Security on Cloud (BigDataSecurity) / 5th IEEE Int Conf on high performance and Smart Computing (IEEE HPSC) / 4th IEEE Int Conf on Intelligent Data and Security (IEEE IDS)

作者： Li, Shuo Lu, Zhonghua Sun, Yongze Deng, Sungen Niu, Beifang Chinese Acad Sci Comp Network Informat Ctr Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

ISBN: (纸本)9781728100067

This paper implements a distributed parameter optimization system based on Mesos and studies the scheduling strategy of this system. Using the resource interface of Mesos, the system packages a variety of common parameter optimization algorithms and task scheduling strategy into a framework software that can run on Mesos. Aiming at the two level scheduling mechanism of Mesos, a dynamic scheduling strategy for distributed parameter optimization system in multi job environment on hybrid deployment cluster is proposed. This paper designs several experiments, and compares the resource scheduling strategy of the architecture software with the FIFO scheduling strategy in the hybrid deployment scenario. This work reduces the difficulty of optimizing distributed parameters in common scenarios such as deep learning in a cluster environment, and improves resource utilization efficiency in multi-task environment.

关键词： Apache Mesos Distributed System Scheduling Parameter optimization Distributed Computing

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performance optimization AND PARALLELIZATION OF TURBO DECODING FOR software-DEFINED RADIO

PERFORMANCE OPTIMIZATION AND PARALLELIZATION OF TURBO DECODI...

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22nd IEEE Canadian conference on Electrical and Computer Engineering

作者： Roth, Jonathan Manjikian, Naraig Sudharsanan, Subramania Queens Univ Dept Elect & Comp Engn Kingston ON K7L 3N6 Canada Digeo Inc Kirkland WA 98033 USA

ISBN: (纸本)9781424435098

This paper describes the optimization, parallelization, and simulated performance of a software double-binary turbo decoder implementation supporting the WiMAX standard suitable for software-defined radio (SDR). Turbo codes offer excellent error-correcting performance, but present high computational requirements, hence a parallel approach is desirable when seeking to exploit the flexibility of SDR. The development of a flexible parallel maximum a postiori (MAP) decoding algorithm is detailed, with simulation speedup results demonstrating good parallel efficiency (above 80%). For the same number of threads, a linear-log-MAP decoder implementation using 4 iterations was shown to be have nearly twice the throughput with comparable BER performance of a max-log-MAP decoder implementation using 8 iterations. In addition to parallel execution, other performance enhancements in software and through customized instructions provide a combined per-thread speedup of up to 57%.

关键词： parallel algorithms hardware simulation turbo decoding software-defined radio (SDR)

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Towards an algorithm and communication cost model for the parallel particle swarm optimization 29th

Towards an algorithm and communication cost model for the pa...

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29th International conference on Architecture of Computing Systems, ARCS 2016

作者： Shuka, Romeo Niemann, Sebastian Brehm, Jürgen Müller-Schloer, Christian Institute of Systems Engineering System and Computer Architecture Leibniz Universität Hannover Hannover Germany

ISBN: (纸本)9783800741571

The adaptation of sequential algorithms for high performance Computing (HPC) systems is determined by a tradeoff between algorithmic effectiveness (software) and communication frequency (hardware) of the parallel implementation (efficiency). To get a better understanding of the correlation, we define simple models for both, software and hardware, in order to dynamically find the best mapping parameters for the execution of the algorithm on the parallel system. For the evaluation of our method we look at population-based algorithms like the Particle Swarm optimization Algorithm (PSO) for the solution of optimization problems. Different goals like best quality of the solution of the optimization problem for a given execution time or best execution time to find the optimum are defined by the user. Our method enables us to find the best parameters for the mapping which then results in an efficient and effective parallel implementation to achieve the user-defined goals on a high performance Computing Cluster (HPCC). © VDE VERLAG GMBH.

关键词： Mapping

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