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An evolutionary optimizing approach to neural network architecture for improving identification and modeling of aircraft nonlinear dynamics

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART G-JOURNAL OF AEROSPACE ENGINEERING 2014年第12期228卷 2178-2191页

作者： Roudbari, Alireza Saghafi, Fariborz Sharif Univ Technol Dept Aerosp Engn Tehran 9821 Iran

In this paper, modified genetic algorithm has been used as a simultaneous optimizer of recurrent neural network to improve identification and modeling of aircraft nonlinear dynamics. Weighted connections, network architecture, and learning rules are features that play important roles in the quality of neural networks training and their generalizability in order to model nonlinear systems. Therefore, the main focus of this paper is to apply appropriate evolutionary methods in order to simultaneously optimize the parameters of neural networks for the improvement identification and modeling of aircraft nonlinear dynamics. To validate this study, the results have been compared with the recorded data from a fourth generation highly maneuverable fighter aircraft flight test. Furthermore, having been compared to normal genetic algorithm, the results of the present study have showed significant improvement of the neural networks generalization which leads to better identification and modeling of aircraft nonlinear dynamics.

关键词： Aircraft nonlinear dynamics system identification neural networks optimization algorithm

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Hydromechanical Analysis of Fluid-Elastomeric Lag Dampers Including Subzero Temperatures

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JOURNAL OF AIRCRAFT 2014年第3期51卷 1056-1061页

作者： Ngatu, Grum T. Singh, Harinder J. Wereley, Norman M. Kothera, Curt S. Univ Maryland Dept Aerosp Engn College Pk MD 20742 USA InnoVital Syst Inc Beltsville MD 20705 USA

The article examined the behavior of a snubber-type fluid-elastomeric (FE) lag damper under dynamic load applications at various operating temperatures. Topics discussed include hydromechanical modeling of fluid-elastomeric damper and parameter identification and model performance. Figures that represent the damping force versus velocity cycles at two different operating temperatures are also presented.

关键词： Helicopter Rotor Fluid Density Material Properties Hysteretic Damping optimization algorithm Aerospace Engineering Laminar Flow Elastomers Snubber Temperature Effects

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Representing Sampled Pareto Frontiers as Parameterized Continuous Manifolds Using Self-Organizing Maps

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AIAA JOURNAL 2014年第11期52卷 2480-2490页

作者： Daskilewicz, Matthew J. German, Brian J. Georgia Inst Technol Sch Aerosp Engn Atlanta GA 30319 USA

This paper describes a method for defining a coordinate system to parameterize a sampled Pareto frontier of a continuous multi-attribute design problem using a modified self-organizing map. By defining such a coordinate system, the design problem may be reformulated from y = f(x) to (y,x) = g(psi), where x is a vector of design variables, y is a vector of attributes, and psi is a vector of barycentric coordinates. Exploration of the design problem using psi as the independent variables has the following desirable properties: 1) Every vector psi corresponds to a Pareto efficient design, and every Pareto efficient design has a corresponding psi. 2) The number of psi coordinates is equal to the number of attributes, regardless of the number of design variables. 3) Each attribute y(i) has a corresponding coordinate psi(i) such that the sign of partial derivative y(i)/partial derivative psi(i) is positive if the objective is to maximize y(i) and negative if the objective is to minimize y(i) (i.e., the attributes improve monotonically as their corresponding coordinates increase). This approach, named the "Pareto simplex self-organizing map" is easily implemented as a postprocessing step to sampling a Pareto frontier with multi-objective optimization. The construction of the coordinates and their use in design space exploration is demonstrated on a conceptual wing design problem.

关键词： Pareto Frontier Space Exploration optimization algorithm Critical Mach Number Bending Moment Artificial Neural Network Evolutionary algorithm Lift to Drag Ratio Transonic Aircraft Genetic algorithm

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Nonlinear Proportional-Derivative Control Incorporating Closed-Loop Control Allocation for Spacecraft

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JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2014年第3期37卷 799-812页

作者： Hu, Qinglei Li, Bo Zhang, Youmin Harbin Inst Technol Dept Control Sci & Engn Harbin 150001 Heilongjiang Peoples R China Concordia Univ Dept Mech & Ind Engn Montreal PQ H3G 1M8 Canada

A novel saturated proportional-derivative control law incorporated with closed-loop control allocation is proposed for spacecraft attitude stabilization in this paper. More specifically, a saturated proportional-derivative-based baseline nonlinear controller is designed to guarantee the globally asymptotic stability under control input/signal constraints. Then, a closed-loop constrained optimal control allocation scheme is employed to distribute the moments synthesized by the baseline controller over the redundant actuators in the presence of constraints due to actuator amplitude and rate constraints. The optimal control solution is to be found by penalizing the control allocation errors and rates using optimal quadratic programming algorithms. A significant feature of this work is that the asymptotic stability with the closed-loop control allocation is guaranteed theoretically. Simulation results are presented to illustrate the performance of the proposed scheme.

关键词： Proportional Derivative Spacecraft Models Actuator Torque Attitude Control System Optimal Control optimization algorithm Spacecraft Attitude Energy Consumption Singular Value Decomposition Nonlinear Control

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An analysis on separability for Memetic Computing automatic design

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INFORMATION SCIENCES 2014年第0期265卷 1-22页

作者： Caraffini, Fabio Neri, Ferrante Picinali, Lorenzo De Montfort Univ Ctr Computat Intelligence Sch Comp Sci & Informat Leicester LE1 9BH Leics England Univ Jyvaskyla Dept Math Informat Technol Jyvaskyla 40014 Finland De Montfort Univ Leicester Media Sch Leicester LE1 9BH Leics England

This paper proposes a computational prototype for automatic design ofa* optimization algorithms. The proposed scheme makes an analysis of the problem that estimates the degree of separability of the optimization problem. The separability is estimated by computing the Pearson correlation indices between pairs of variables. These indices are then manipulated to generate a unique index that estimates the separability of the entire problem. The separability analysis is thus used to design thea* optimization algorithm that addresses the needs of the problem. This prototype makes use of two operators arranged in a Parallel Memetic Structure. The first operator performs moves along the axes while the second simultaneously perturbs all the variables to follow the gradient of the fitness landscape. The resulting algorithmic implementation, namely Separability Prototype for Automatic Memes (SPAM), has been tested on multiple testbeds and various dimensionality levels. The proposed computational prototype proved to be a flexible and intelligent framework capable to learn from a problem and, thanks to this learning, to outperform modern meta-heuristics representing the-state-of-the-art in optimization. (C) 2014 Elsevier Inc. All rights reserved.

关键词： Memetic Computing optimization algorithm algorithmic design Computational intelligence optimization

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An efficient algorithm for function optimization: modified stem cells algorithm

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OPEN ENGINEERING 2013年第1期3卷 36-50页

作者： Taherdangkoo, Mohammad Paziresh, Mahsa Yazdi, Mehran Bagheri, Mohammad Hadi Shiraz Univ Fac Elect & Comp Engn Dept Commun & Elect Shiraz Iran Australian Natl Univ RSISE Canberra ACT 0200 Australia Harvard Med Sch Brigham & Womens Hosp Dept Radiol Ctr Evidence Based Imaging Brookline MA USA

In this paper, we propose ana* optimization algorithm based on the intelligent behavior of stem cell swarms in reproduction and self-organization.a* optimization algorithms, such as the Genetic algorithm (GA), Particle Swarm optimization (PSO) algorithm, Ant Colony optimization (ACO) algorithm and Artificial Bee Colony (ABC) algorithm, can give solutions to linear and non-linear problems near to the optimum for many applications;however, in some case, they can suffer from becoming trapped in local optima. The Stem Cells algorithm (SCA) is ana* optimization algorithm inspired by the natural behavior of stem cells in evolving themselves into new and improved cells. The SCA avoids the local optima problem successfully. In this paper, we have made small changes in the implementation of this algorithm to obtain improved performance over previous versions. Using a series of benchmark functions, we assess the performance of the proposed algorithm and compare it with that of the other aforementioneda* optimization algorithms. The obtained results prove the superiority of the Modified Stem Cells algorithm (MSCA).

关键词： optimization algorithm Modified stem cells algorithm Particle swarm optimization Ant colony optimization Artificial bee colony algorithm Genetic algorithm

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Using a Potential Game for Power Reduction in Distributed Storage Systems 2

Using a Potential Game for Power Reduction in Distributed St...

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IEEE International Conference on Cloud Engineering (IC2E)

作者： Hasebe, Koji Sawada, Takumi Kato, Kazuhiko Univ Tsukuba Dept Comp Sci Tsukuba Ibaraki 3058573 Japan

ISBN: (纸本)9781479937660

We present a game-theoretic approach for power reduction in large-scale distributed storage systems. The key idea is to use distributed hash tables to dynamically migrate virtual nodes, thus skewing the workload towards a subset of physical disks without overloading them. To realize this idea in an autonomous way (i.e., without any kind of central controller), virtual nodes are considered to be selfish agents playing a game in which each node receives a payoff according to the workload of the disk on which it currently resides. We model this setting as a potential game, where an increase in the payoff to a virtual node reduces the power of the system. This game consists of a pair of global and private utility functions, derived by means of the Wonderful Life Utility technique. The former function evaluates the state of the system, and the latter provides criteria for the migration of each node. The performance of our method is measured by simulations and a prototype implementation. From these evaluations, we find that our method reduces the running time of the disks in active mode by 12.7-18.7%, with an overall average response time of 50-190 ms.

关键词： Computational modeling Games Load modeling Power demand Prototypes Scalability Time factors Autonomous control Distributed storage system Game theory optimization algorithm Power saving Game theory autonomous control power reduction Cache Memory System Power demand Load modeling optimization algorithms Time Factors Computational modeling Scalability PROTOTYPE Game animals disks Hunting Workload

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Optimized Modelling of Maternal ECG Beats using the Stationary Wavelet Transform 41

Optimized Modelling of Maternal ECG Beats using the Stationa...

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41st Computing in Cardiology Conference (CinC)

作者： Andreotti, Fernando Behar, Joachim Oster, Julien Clifford, Gari D. Malberg, Hagen Zaunseder, Sebastian Tech Univ Dresden Inst Biomed Engn Fac Elect & Comp Engn D-01062 Dresden Germany Univ Oxford Dept Engn Sci Inst Biomed Engn Oxford OX1 3PJ England Emory Univ Dept Biomed Informat Atlanta GA 30322 USA Emory Univ Dept Biomed Engn Atlanta GA 30322 USA Georgia Inst Technol Atlanta GA 30332 USA

ISBN: (纸本)9781479943463

Introduction: The ECG Bayesian filtering framework has been shown to be a promising method to extract the foetal electrocardiogram (FECG) from abdominal recordings. This framework requires an estimation of the ECG morphology, which is obtained by approximating an average beat with a number of Gaussian kernels. This approximation results in a high dimensional nonlinear optimization problem (finding ideal positions, width and height for these kernels). Methods: Proposed methodologies in the literature initialize thea* optimization algorithm using fixed positions for the kernel functions. This contribution benchmarks alternative schemes for finding the Gaussian parameters, namely an approach based on the stationary wavelet transform and random search. The goal is minimizing the normalized mean squared error between the average beat and the approximated model, while increasing foetal QRS detection accuracy. Results: The suggested methods are able to produce improved morphology approximations of the averaged beat up 4.05% (depending on the selected method). The proposed method using the stationary wavelet transform improves the goodness of the fit, while reducing the computational load. However, no immediate improvement on the accuracy of FQRS detections was noticed. Such findings render the proposed method a promising tool. However, further research should be directed at transferring the improved fit to an improvement of FQRS detections.

关键词： Bayes methods Gaussian processes electrocardiography mean square error methods medical signal detection obstetrics optimisation wavelet transforms ECG Bayesian filtering framework ECG morphology estimation FECG FQRS detection Gaussian kernels Gaussian parameters abdominal recordings approximated model average beat computational load fixed positions foetal QRS detection accuracy foetal electrocardiogram high dimensional nonlinear optimization problem improved morphology approximations kernel functions maternal ECG beats normalized mean squared error optimization algorithm optimized modelling random search stationary wavelet transform Accuracy Approximation methods Computational modeling Electrocardiography Fitting Kernel optimization wavelet transforms Electrocardiography Mean square error medical signal detection KERNEL FUNCTIONS optimization algorithms Approximation method Bayes methods Gaussian processes Computational modeling Gynecology Kernel MACHINE SHOP PRACTICE Seeds

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Comprehensive Parameters Adaption of Rolling Force Model Based on Objective Function in Tandem Cold Mill

Comprehensive Parameters Adaption of Rolling Force Model Bas...

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International Conference on Design, Manufacturing and Mechatronics (ICDMM)

作者： Bu, He-Nan Yan, Zhu-Wen Zhang, Cheng-Ming Zhang, Dian-Hua Northeastern Univ State Key Lab Rolling & Automat Shenyang Liaoning Peoples R China Bengang Steel Plates Co Ltd High Strength Steel Dev Dept Shenzhen Liaoning Peoples R China

ISBN: (纸本)9783038351016

In order to improve the setting accuracy of rolling force model, a comprehensive parameters adaptive objective function of rolling force was established. During the adaptive process, the adaptive coefficients of friction coefficient and deformation resistance were taken as the optimized parameters, and the Rosenbrock algorithm was used to search the optimal solution of the objective function. The proposed adaptive method has been applied successfully to a 1450mm 5-stand tandem cold mill. Application results show that the forecast accuracy of rolling force model has been improved significantly.

关键词： Tandem cold mill Objective function Rolling force model optimization algorithm Adaption

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Two Local Search Components that Move Along the Axes for Memetic Computing Frameworks

Two Local Search Components that Move Along the Axes for Mem...

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2014 IEEE Symposium on Foundations of Computational Intelligence (FOCI)

作者： Neri, Ferrante Khan, Noel De Montfort Univ Ctr Computat Intelligence Sch Comp Sci & Informat Gateway Leicester LE1 9B1L Leics England

ISBN: (纸本)9781479944910

Within memetic computing frameworks, the structure as well as a correct choice of memes are important elements that drive successfula* optimization algorithms. This paper studies variations of a promising yet simple search operator, the S algorithm, which can easily be integrated within a memetic framework to improve candidate solutions. S is a single-solution optimizer that iteratively perturbs variables and conditionally evaluates solutions along the axes. The first S variant, namely S2, unconditionally evaluates solutions in both directions while S3 maintains D uncorrelated step sizes that are either expanded in the direction of improving fitness or else redirected and contracted. Numerical results from the CEC2010 and CEC2014 benchmarks show that the variants outperform S in terms of the number of function evaluations for a given fitness value and, further, that S3 outperforms S in terms of final fitness against a wide range of problems and dimensionality.

关键词： evolutionary computation iterative methods optimisation search problems CEC2010 benchmark CEC2014 benchmark S algorithm conditional evaluation function evaluation iterative variable perturbation local search components memetic computing frameworks optimization algorithm search operator single-solution optimizer algorithm design and analysis Benchmark testing Correlation Market research Memetics optimization Sociology Function evaluation algorithm design and analysis Benchmark testing optimization algorithms tabu search market research Sociology evolutionary algorithm iterative methods axe Axis, Cervical Vertebra Sulfur Frameworks

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