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Supersonic Inlet Control Design for Integrated Aero Propulsion during Supersonic Cruising State

TRANSACTIONS OF THE JAPAN SOCIETY FOR AERONAUTICAL AND SPACE SCIENCES 2016年第6期59卷 349-355页

作者： Sun, Fengyong Zhang, Haibo Nanjing Univ Aeronaut & Astronaut Coll Energy & Power Engn Jiangsu Prov Key Lab Aerosp Power Syst Nanjing 210016 Jiangsu Peoples R China

In order to solve the supersonic inlet control problems during supersonic cruising state, an integrated model and inexact one-dimensional search algorithm are devised to design the inlet control law. An online inlet ramp angle mapping model is also proposed using recursive reduced least squares support vector regression and BP neural network methods. Finally, a proportional and integral (PI) controller is designed to control the inlet ramp. The simulation results indicated that an effective inlet ramp controller would greatly improve the engine installed performance and protect the engine from surge and other dangerous operations.

关键词： Integrated Model Inlet Control Law support vector regression machine Propulsion Installed Performance

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Recognition of rolling bearing fault patterns and sizes based on two-layer support vector regression machines

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SMART STRUCTURES AND SYSTEMS 2014年第3期13卷 453-471页

作者： Shen, Changqing Wang, Dong Liu, Yongbin Kong, Fanrang Tse, Peter W. City Univ Hong Kong Dept Syst Engn & Engn Management Hong Kong Hong Kong Peoples R China Univ Sci & Technol China Sch Engn Sci Hefei 230026 Peoples R China

The fault diagnosis of rolling element bearings has drawn considerable research attention in recent years because these fundamental elements frequently suffer failures that could result in unexpected machine breakdowns. Artificial intelligence algorithms such as artificial neural networks (ANNs) and support vector machines (SVMs) have been widely investigated to identify various faults. However, as the useful life of a bearing deteriorates, identifying early bearing faults and evaluating their sizes of development are necessary for timely maintenance actions to prevent accidents. This study proposes a new two-layer structure consisting of support vector regression machines (SVRMs) to recognize bearing fault patterns and track the fault sizes. The statistical parameters used to track the fault evolutions are first extracted to condense original vibration signals into a few compact features. The extracted features are then used to train the proposed two-layer SVRMs structure. Once these parameters of the proposed two-layer SVRMs structure are determined, the features extracted from other vibration signals can be used to predict the unknown bearing health conditions. The effectiveness of the proposed method is validated by experimental datasets collected from a test rig. The results demonstrate that the proposed method is highly accurate in differentiating between fault patterns and determining their fault severities. Further, comparisons are performed to show that the proposed method is better than some existing methods.

关键词： statistical parameters bearing fault diagnosis deterioration evaluation a two-layer structure support vector regression machine

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X-ray image distortion correction based on SVR

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Journal of Measurement Science and Instrumentation 2015年第3期6卷 302-306页

作者：袁泽慧李世中中北大学机电工程学院山西太原030051

X-ray image has been widely used in many fields such as medical diagnosis,industrial inspection,and so ***,due to the physical characteristics of X-ray and imaging system,distortion of the projected image will happen,which restrict the application of X-ray image,especially in high accuracy *** correction can be performed using algorithms that can be classified as global or local according to the method used,both having specific advantages and *** this paper,a new global method based on support vector regression（SVR）machine for distortion correction is *** order to test the presented method,a calibration phantom is specially designed for this purpose.A comparison of the proposed method with the traditional global distortion correction techniques is *** experimental results show that the proposed correction method performs better than the traditional global one.

关键词： X-ray image distortion correction support vector regression machine

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Design of an Ultrawide Band Low Noise Microstrip Amplifier Using 3D Sonnet- Based SVRM with Particle Swarm Optimization for Space Applications

Design of an Ultrawide Band Low Noise Microstrip Amplifier U...

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6th International Conference on Recent Advances in Space Technologies (RAST)

作者： Demirel, Salih Gunes, Filiz Keskin, Ahmet Kenan Yildiz Tech Univ Dept Elect & Commun Engn Istanbul Turkey

ISBN: (纸本)9781467363969;9781467363952

In this work, a determinististic, efficient design methodology is put forward to design a wide-band, low-noise microstrip amplifier, where the microstrip widths, lengths {(W) over right arrow, (l) over right arrow} of the input/ output matching networks are obtained accurately and fast for a substrate {epsilon(r), h, tan delta} using the cost-effective 3D EM- based support vector regression machine (SVRM) microstrip model provided that ensuring the stable source Z(S)(omega) and load Z(L)(omega) terminations for the compatible {Noise F(omega) >= F-min(omega), Input VSWR Vi(omega) >= 1, Gain G(Tmin)(omega)>G(T)(omega)>= G(Tmax)(omega), Bandwidth B} quadrates of the employed transistor. The 3D EM- based SVRM microstrip model provides the accurate and fast characterization of the equivalent transmission line in terms of the characteristic impedance Z(0) and the dielectric constant epsilon(eff) within the continuous domain of {0.1mm <= W <= 4.6 mm, 2 <= epsilon(r) <= 10, 0.1mm <= h <= 2.2mm, 2GHz <= f <= 14GHz} in an efficient manner. In the modeling process, the substantial reduction (up to % 64) is obtained utilizing sparseness of SVRM in the number of expensive fine discretization training data with the negligible loss in the predictive accuracy using the quasi-TEM microstrip synthesis formulas as the coarse model that allow to identify the regions of the design space requiring denser sampling. Moreover, the multi-objective amplifier design problem is reduced into the two single-objective design problems of the input(IM)/ output (OM) matching networks to provide the source Z(S)(omega) and load Z(L)(omega) terminations to the transistor, respectively. Finally the design methodology is applied to the design of typical wideband low-noise amplifiers of the transistor NE3512S02 within 3GHz and 8GHz using T-, II- L types of microstrip matching circuits satisfying the maximum gain provided the available minimum noise and a permitted amount of input mismatching at each operat

关键词： support vector regression machine Microwave Amplifier Transducer Gain Noise Figure Input VSWR Microstripline

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Forex prediction based on SVR optimized by artificial fish swarm algorithm

Forex prediction based on SVR optimized by artificial fish s...

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4th Global Congress on Intelligent Systems (GCIS)

作者： Ma Li Fan Suohai Jinan Univ Dept Math Sch Informat Sci & Technol Guangzhou Guangdong Peoples R China

ISBN: (纸本)9781479928859

Taking the radial basis function as a kernel function, a prediction model is developed based on the support vector regression machine (SVR). The optimization of the model parameters, including penalty factor and kernel function variance, is realized by the artificial fish swarm algorithm. The model is used to predict nine foreign exchange rate data with updating and rolling. At the same time, simulating by the cross validation, genetic algorithm, particle swarm optimization algorithm and then evaluating the results from the total error (TE), relative error (RE), absolute root mean square error (ARMSE) and correct trend rate (CTR) comprehensively, the comparison shows that the errors of the model based on SVR optimized by artificial fish swarm algorithm are all minimum and CTR are maximum. In the end, in order to improve the convergence speed and precision further, the self-adaption artificial fish swarm algorithm is presented which is joined the attenuation factor and based on the average distance visual. The result is ideal. Therefore, SVR optimized by the improved artificial fish swarm algorithm can be effectively used in forex prediction.

关键词： support vector regression machine Artificial Fish Swarm Algorithm Penalty factor Kernel function variance Forex prediction

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Study on Coal Logistics Demand Forecast Based on PSO-SVR

Study on Coal Logistics Demand Forecast Based on PSO-SVR

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10th International Conference on Service Systems and Service Management (ICSSSM)

作者： Chen Pei-you Liu Lu Heilongjiang Inst Sci & Technol Coll Econ Management Heilongjiang 150027 Harbin Peoples R China Heilongjiang Inst Sci & Technol Coll Grad Studies Heilongjiang 150027 Harbin Peoples R China

ISBN: (纸本)9781467348430

The coal logistics demand in this paper is refer to the demand of coal transportation, mainly including: the railway, highway and waterway freight volume of coal. In consideration of the small and the nonlinear history sample, this paper combines the support vector regression machine (support vector regression, SVR) and Particle Swarm Optimization algorithm, (Particle Swarm Optimization, PSO) to propose PSO-SVR coal logistics demand forecasting model which is suitable for the learning of small samples. Taking Coal railway freight volume for example, the paper first select influence factors and coal railway freight volumes from 1995 to 2011 as the learning samples to establish the "influence factors - coal railway freight volume" SVR model and then use the particle swarm algorithm to optimize model parameters, Finally, it forecasts the coal railway freight volume. The results show that the prediction accuracy of PSO-SVR model is superior to the BP neural network model.

关键词： support vector regression machine particle swarm algorithm coal railway freight volume forecasting

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Comprehensive Evaluation of Wind Turbine Type Selection Based on GA-SVR Model

Comprehensive Evaluation of Wind Turbine Type Selection Base...

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3rd International Conference on Manufacturing Science and Engineering (ICMSE 2012)

作者： Sun, Wei Shen, Le N China Elect Power Univ Baoding Peoples R China

ISBN: (纸本)9783037853696

Aiming at the current situation of wind turbine type selection in China, this paper has built a more scientific and systematic index system for comprehensive evaluation of wind turbine type selection, and also applied the support vector regression machine evaluation model with parameters optimized by Genetic Algorithm. Through automatic global optimization for parameters, this model has reached an extremely high accuracy required for evaluation of type selection. Empirical analysis shows that the application of this model has a realistic popularized significance for improving the method of the wind turbine type selection and enhancing its efficiency.

关键词： wind turbine type selection Genetic Algorithm support vector regression machine

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Research on Performance of Audio Frequency Equalizer Evaluation Based on support vector regression

Research on Performance of Audio Frequency Equalizer Evaluat...

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22nd Chinese Control and Decision Conference

作者： Zhang, Zhiqiang Zhang, Aihua BoHai Univ Coll Informat Sci & Engn Jinzhou 121000 Peoples R China

ISBN: (纸本)9781424451821

A evaluate method of audio frequency equalizer performance based on support vector machine was presented. Taking BPF that applied to audio frequency equalizer as experiment object. First, obtaining dataset of the amplitude frequency of the BPF by amplitude frequency characteristics testing equipment to get samples, and then using support vector regression to get the approach function of the amplifier amplitude frequency characteristics of the BPF, using the function to test four performance parameters. The experimental results show that this method improves the precision of the parameters testing. It is suitable for the evaluate of the electronic production performance that tested by oscillograph.

关键词： support vector regression machine Performance of Audio Frequency Equalizer Evaluation Amplifier Amplitude Frequency Characteristics

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The seam offset identification based on support vector regression machines

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China Welding 2009年第2期18卷 75-80页

作者：曾松盛石永华王国荣黄国兴 School of Mechanical and Automotive Engineering South China University of Technology Guangzhou 510640

The principle of the support vector regression machine（SVR） is first analysed. Then the new data-dependent kernel function is constructed from information geometry perspective. The current waveforms change regularly in accordance with the different horizontal offset when the rotational frequency of the high speed rotational arc sensor is in the range from 15 Hz to 30 Hz. The welding current data is pretreated by wavelet filtering, mean filtering and normalization treatment. The SVR model is constructed by making use of the evolvement laws, the decision function can be achieved by training the SVR and the seam offset can be identified. The experimental results show that the precision of the offset identification can be greatly improved by modifying the SVR and applying mean filteringfrom the longitudinal direction.

关键词： support vector regression machine data-dependent kernel function offset identification mean filtering

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Research on Performance of Audio Frequency Equalizer Evaluation Based on support vector regression

Research on Performance of Audio Frequency Equalizer Evaluat...

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The 22nd China Control and Decision Conference(2010年中国控制与决策会议)

作者： Zhiqiang Zhang Aihua Zhang College of Information Science and Engineering BoHai University JinZhou 121000

ISBN: (纸本)9781424451814;9781424451821

关键词： support vector regression machine Performance of Audio Frequency Equalizer Evaluation Amplifier Amplitude Frequency Characteristics

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