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2010 4th International Conference on Intelligent Information Technology Application (IITA2010)

作者： He Yong-mei Jilin University of Finance and Economics Changchun 130117,China Guo Qin Jilin University of Finance and Economics Changchun 130117,China

Tax prediction is very significant to make up the economy policy and adjust the structure of economy.A hybrid model of RBF neural network and particle swarm optimization algorithm is proposed to predict tax in the *** connection weights which are connected hidden layer and output layer,and the centers and the widths of radial basis function in hidden layer are selected by particle swarm optimization to solve the influence of them on prediction ability of RBF neural *** error of tax prediction between PSO-RBFNN and RBFNN is computed,which illustrates that the tax prediction ability of PSO-RBFNN is better than that of RBFNN.

关键词： tax prediction optimization algorithm neural network particle swarm optimization prediction technology

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Straight Wedge Absorber Geometry optimization for Normal and Oblique Incidence 10

Straight Wedge Absorber Geometry Optimization for Normal and...

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Loughborough Antennas and Propagation Conference (LAPC)

作者： Pometcu, Laura Sharaiha, Ala Benzerga, Ratiba Pouliguen, Philippe Univ Rennes 1 Inst Elect & Telecommun Rennes Rennes France DGA DS Off Adv Res & Innovat Bagneux France

ISBN: (纸本)9781479936625

This paper presents a topology for improving the performance of straight wedge absorbing materials at oblique angles of incidence. The optimization process is based on a genetic algorithm and allows one to obtain a low reflection coefficient within a wide band of frequencies. We investigate the performance by comparison between the straight wedge absorber and the optimized wedge absorber. These wedge absorbers were simulated in the CST Microwaves Studio software, using a polymer foam material.

关键词： straight wedge absorber polymer foam anechoic chamber optimization algorithm normal incidence oblique incidence

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Synthesis of Aperiodic Linear Antenna Arrays Based on Competition Over Resources optimization 6

Synthesis of Aperiodic Linear Antenna Arrays Based on Compet...

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IEEE Asia Pacific Conference Appl Electromagnetics

作者： Gholami, Reza Zakeri, Bijan Mohseni, Sina Abedi, Hajar Babol Noshirvani Univ Technol Fac Elect Engn Babol Sar Iran

ISBN: (纸本)9781479966035

In the recent years, many heuristic optimization algorithms derived from the behavior of biological or physical systems in nature have been developed. In this paper, we propose a new optimization algorithm based on competitive behavior of group animals. The competition gradually results in an increase in population of wealthy group which gives a fast convergence to the optimization algorithm. In the following, after a detailed explanation of the algorithm and pseudo code, we compare it with particle swarm optimizations as a famous heuristic algorithm. The proposed method is used to determine an optimal set of amplitude weights of antennas and element spacing that satisfy the optimal goal during constant first null beamwidth (FNBW) for aperiodic linear array. It is to be noted that the desired prescribed nulls depth and side lobes level is achieved simultaneously with the narrowest possible FNBW. The proposed algorithm on known array antenna synthesis, shows faster and superior results compared to other optimization algorithms.

关键词： Aperiodic linear array competition over resources null control optimization algorithm pattern synthesis

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Binary sequence design for transmit diversity MIMO radar

Binary sequence design for transmit diversity MIMO radar

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IET International Radar Conference 2015

作者： Haiqing Wang Xiaohua Zhu Jindong Zhang College of Electronic Engineering and Optical Technology Nanjing University of Science and Technology Jiangsu China 210094 College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Jiangsu China 210094

ISBN: (纸本)9781510822023

Orthogonal binary sequences play an important role in Multiple-input multiple-output (MIMO) radar. However, mainly due to mathematical difficulties, the synthesis problem of multiple binary sequences with low auto and cross correlation sidelobes is still an open problem. In this paper, mismatched filter, which basically aims at reducing the sidelobe levels at the pulse compression output, is exploited and multiple binary sequences and mismatched filters are optimized by joint synthesis algorithms (JSA), based on an alternating direction method. JSA for minimizing the integrated sidelobe level (ISL) and peak sidelobe level (PSL) metrics are proposed, and the corresponding computation complexities are analyzed. Simulation results show the effectiveness of our proposed algorithms.

关键词： Binary sequence design MIMO radar optimization algorithm MIMO radar optimization algorithms binary sequences side lobe level JOB SAFETY ANALYSIS sidelobe diversity reception

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Flight Vehicle System Identification: A Time-Domain Methodology, Second Edition Second edition.

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丛书名： Progress in Astronautics and Aeronautics

2015年

作者： Ravindra V. Jategaonkar

ISBN: (数字)9781624102790

ISBN: (纸本)9781624102783

Flight Vehicle System Identification, Second Edition offers a systematic approach to flight vehicle system identification and covers exhaustively the time-domain methodology. Beginners, as well as practicing engineers, researchers, and working professionals who wish to refresh or broaden their knowledge of flight vehicle system identification, will find this book highly *** addresses in detail the theoretical and practical aspects of various parameter estimation methods, including those in the stochastic framework focusing on nonlinear models, cost function, optimization methods, and residual analysis. A pragmatic and balanced account of pros and cons in each case is provided. The book also presents data gathering and model validation, covering both large-scale and high-fidelity ***-world problems dealing with a variety of flight vehicle applications are addressed, and solutions are provided. Examples encompass such problems as estimation of aerodynamic stability and control derivatives from flight data, flight-path reconstruction, nonlinearities in control surface effectiveness, stall hysteresis, unstable aircraft, flexible aircraft model integrating rigid-body and structural dynamics, wake vortex encounters, and other critical *** on the author's years of experience, Flight Vehicle System Identification, Second Edition also provides recommendations for overcoming problems likely to be faced in developing complex nonlinear and high-fidelity models and can help the novice negotiate the challenges of developing highly accurate mathematical models and aerodynamic databases from experimental flight *** that runs under MATLAB and sample flight data are provided to assist the reader in reworking the examples presented in the text. The extended software is highly flexible to use and suitable for advanced applications; it can also be adapted to the reader's own *** second edition retains the same structure as the firs

关键词： Aerodynamic Performance Aerodynamics Aeronautical Engineering Aeronautics Aerospace Sciences Aircraft Components and Structure Aircraft Control Aircraft Design Aircraft Flight Control System Aircraft Maneuvers Aircraft Operations Aircraft Operations and Technology Aircraft Stability and Control Aircraft Systems Aircraft Wing Components Aircraft Wing Design Aircrafts Aircrew Airspeed algorithms and Data Structures Artificial Neural Network Atmospheric Science Aviation Aviation Safety Avionics Avionics Computers Avionics Software Civil Aviation Commercial Aviation Computational Fluid Dynamics Computer Programming and Language Computer Storage Devices Computing System Computing and Informatics Computing, Information, and Communication Data Analysis Data Analytics Data Science Finite Difference Method Fixed-Wing Aircraft Flight Control Surfaces Flight Recorder Flight Test Flight Training Fluid Dynamics Flying Qualities Guidance, Navigation, and Control Systems Helicopters Longitudinal Static Stability Military Aircraft Military Aviation Monte Carlo Method Numerical Analysis optimization algorithm Rotorcrafts Runge-Kutta Methods Short Takeoff and Landing Signal Processing Slip (Aerodynamics) Software Systems Stability Augmentation Systems Takeoff and Landing Turbulence Turbulence Models Unmanned Aerial Vehicle Vortex Dynamics Wind Tunnels Wing Configurations Wing Planforms

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SpecMonitor: Toward Efficient Passive Traffic Monitoring for Cognitive Radio Networks

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2014年第10期13卷 5893-5905页

作者： Yan, Qiben Li, Ming Chen, Feng Jiang, Tingting Lou, Wenjing Hou, Y. Thomas Lu, Chang-Tien Virginia Polytech Inst & State Univ Blacksburg VA 24061 USA Utah State Univ Logan UT 84322 USA SUNY Albany Albany NY 12222 USA

Passive monitoring by distributed wireless sniffers has been used to strategically capture the network traffic, as the basis of automatic network diagnosis. However, the traditional monitoring techniques fall short in cognitive radio networks (CRNs) due to the much larger number of channels to be monitored and the secondary users' channel availability uncertainty imposed by primary user activities. To better serve CRNs, we propose a systematic passive monitoring framework, i.e., SpecMonitor, for traffic collection using a limited number of sniffers inWi-Fi-like CRNs. We jointly consider primary user activity and secondary user channel access pattern to optimize the traffic capturing strategy. In particular, we exploit a nonparametric density estimation method to learn and predict secondary users' access pattern in an online fashion, which rapidly adapts to the users' dynamic behaviors and supports accurate estimation of merged access patterns from multiple users. We also design near-optimal monitoring algorithms that maximize two levels of quality-of-monitoring goals based on the predicted channel access patterns. The simulations and experiments show that SpecMonitor outperforms the existing schemes significantly.

关键词： Cognitive radio network passive monitoring non-parametric density estimation optimization algorithm

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Optimal Removal of Experimental Points to Determine Apparent Thermal Diffusivity of Canned Products

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INTERNATIONAL JOURNAL OF FOOD ENGINEERING 2014年第2期10卷 223-231页

作者： da Silva, Wilton P. Silva, Cleide M. D. P. S. Lins, Marcos A. A. da Costa, Waldemir S. Univ Fed Campina Grande Dept Phys Campina Grande Paraiba Brazil

To describe the transient heat conduction from or to a product, its thermo-physical properties must be known. If the boundary condition of the heat conduction equation is of the first kind, the process is governed by the thermal diffusivity alpha. Normally this property is determined by fit of the analytical solution with only the first term of the series to an experimental dataset of the temperature versus time, in which the temperature is measured in a known position. In this case, the value obtained for alpha contains errors due to the consideration of only one term and the inclusion of the first experimental points in the fit. This article presents an algorithm based on optimal removal of experimental points to minimize errors in the determination of alpha. The algorithm was validated and applied to heating of Agar gel. The precision and accuracy of the obtained result were, respectively, 0.38 and 0.6%.

关键词： transient heat transfer heat conduction equation heating kinetics simulation equilibrium boundary condition optimization algorithm

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Sparse Representation with Optimized Learned Dictionary for Robust Voice Activity Detection

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CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2014年第7期33卷 2267-2291页

作者： You, Datao Han, Jiqing Zheng, Guibin Zheng, Tieran Li, Jie Harbin Inst Technol Sch Comp Sci & Technol Harbin 150001 Peoples R China Henan Univ Kaifeng 475001 Peoples R China

Traditionally, most of voice activity detection (VAD) methods are based on speech features such as spectrum, temporal energy, and periodicity. The robustness of these features plays a critical role on the performance of VAD. However, since these features are always directly generated from observed signal, the robustness of these features would be significantly degraded in non-stationary noise environments, especially at low level signal-to-noise ratio (SNR) condition. This paper proposes a kind of robust feature for VAD based on sparse representation with an optimized learned dictionary. To do so, a speech dictionary and a noise dictionary are first learned from speech corpus and noise corpus, respectively. Then an optimization algorithm is designed to reduce the mutual coherence between the two learned dictionaries. After that the proposed feature is generated from the optimized dictionary-based sparse representation, and a VAD method is derived from the proposed feature. The proposed method is evaluated over seven types of noise and four types of SNR level, experimental results show that the optimized dictionary is important for enhancing the robustness of the proposed method, and the proposed method performs well under non-stationary noise, especially at low level SNR condition.

关键词： Sparse representation Learned dictionary optimization algorithm Mutual coherence Voice activity detection (VAD)

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A consensus framework for multiple attribute group decision analysis in an evidential reasoning context

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INFORMATION FUSION 2014年第1期17卷 22-35页

作者： Fu, Chao Huhns, Michael Yang, Shanlin Hefei Univ Technol Sch Management Hefei 230009 Anhui Peoples R China Minist Educ Key Lab Proc Optimizat & Intelligent Decis Making Hefei 230009 Anhui Peoples R China Univ S Carolina Dept Comp Sci & Engn Columbia SC 29208 USA

In group decision analysis, consensus has usually been reached by one of two strategies, modifying assessments of experts and adjusting weights of experts. Due to lack of attention paid to the unauthentic change and neglect of experts' assessments, respectively, this paper develops a consensus framework to combine them. The consensus framework is implemented in an evidential reasoning context. It can deal effectively with the situation of missing assessments on specific attributes (the attributes are called missing attributes), which may be caused by lack or limitation of knowledge, experience, and available data about the problem domain. The recommendations generated based on the idea of reaching the maximal consensus on missing attributes and group discussion help experts to give effective assessments on missing attributes. Furthermore, the consensus framework contains a feedback mechanism to provide guidance for experts in order to accelerate convergence to consensus. Identification rules at three levels, including the attribute, alternative and global levels, and a suggestion rule are involved in the feedback mechanism. The former indicates that specific experts are recommended to renew their identified assessments damaging consensus, and the latter generates appropriate recommendations for the experts to renew their assessments. If consensus is still not reached after two consecutive rounds of recommendation generating and assessment renewing, then optimization algorithms still constructed at three levels are used to adjust subjective weights of experts so as to facilitate convergence to consensus. An engineering project management software selection problem is solved by the consensus framework to demonstrate its detailed implementation process, validity, and applicability. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Multiple attribute group decision analysis Evidential reasoning approach Consensus framework Missing assessment Feedback mechanism optimization algorithm

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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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