Intelligent modeling and identification of aircraft nonlinear flight

In this paper, a new approach has been proposed to identify and model the dynamics of a highly maneuverable fighter aircraft through artificial neural networks（ANNs）. In general, aircraft flight dynamics is considered as a nonlinear and coupled system whose modeling through ANNs, unlike classical approaches, does not require any aerodynamic or propulsion information and a few flight test data seem sufficient. In this study, for identification and modeling of the aircraft dynamics, two known structures of internal and external recurrent neural networks（RNNs） and a proposed structure called hybrid combined recurrent neural network have been used and compared.In order to improve the training process, an appropriate evolutionary method has been applied to simultaneously train and optimize the parameters of ANNs. In this research, it has been shown that six ANNs each with three inputs and one output, trained by flight test data, can model the dynamic behavior of the highly maneuverable aircraft with acceptable accuracy and without any priori knowledge about the system.     

基于多层感知器神经元的空间柔性机器人位置跟踪控制

针对基体位置及姿态均不受控的自由漂浮柔性空间机器人轨迹跟踪问题,提出了一种前馈多层感知器(MLP)神经网络控制策略.建立了末端柔性的自由漂浮基机器人的耦合动力学模型,再利用MLP神经网络良好的逼近能力来自适应补偿非线性柔性臂的逆动力学模型,其误差代价函数由PID控制器提供,权重及阀值的调整采用改进的BP反传算法.最后通过仿真比较详细分析了所提方案的工作机理及对非线性强耦合系统控制的有效性.     

Double-stage Metamodel and Its Application in Aerodynamic Design Optimization

Constructing metamodel with global high-fidelity in design space is significant in engineering design. In this paper, a double-stage metamodel (DSM) which integrates advantages of both interpolation metamodel and regression metamodel is constructed. It takes regression model as the first stage to fit overall distribution of the original model, and then interpolation model of regression model approximation error is used as the second stage to improve accuracy. Under the same conditions and with the same samples, DSM expresses higher fidelity and represents physical characteristics of original model better. Besides, in order to validate DSM characteristics, three examples including Ackley function, airfoil aerodynamic analysis and wing aerody-namic analysis are investigated. In the end, airfoil and wing aerodynamic design optimizations using genetic algorithm are presented to verify the engineering applicability of DSM.     

一类Cohen-Grossberg型BAM脉冲神经网络周期解的存在性和全局指数稳定性

利用Lyapunov泛函、不等式方法及Poincaré映射,建立了一类具有混合时滞的Cohen-Grossberg型BAM脉冲神经网络周期解的存在性和全局指数稳定性的充分性判据。     

航空型号项目风险预测的BP神经网络模型及应用

本研究克服了单纯采用专家风险因子测度方法主观性较强的缺点,以及单纯采用人工神经网络评估模型模糊性的缺点,结合两种方法的优点,利用基于BP神经网络算法的Microsoft Visual C++程序,在专家风险因子测度基础上,通过大量风险评估成功案例数据的训练,成功建立了航空型号项目风险预测模型.该模型可以较为精确、客观地...     

基于神经网络的超精密车床圆柱度误差补偿技术研究

根据所研制的超精密亚微米车床的具体特点，提出了一种基于神经网络的工件圆柱度误差补偿方法，并据此建立了误差补偿控制系统，实验结果证明所提方法简单易行，在超精密车削中应用前景广阔。     

决策支持系统中基于神经网络的非线性预测方法

本文说明了在决策支持系统中引入人人工神经网络模型进行预测的原因，并介绍了在DSS中用于非线性预测所采用的神经网络模型，以及基于这种网络模型进行非线性预测的基本原理和具体的学习算法。     

基于多处理机系统在超立方体上并行仿真BP神经网

摘 要 本文介绍了人工神经网络的基本实现途径，并对 Intel iPSC860／P超级计算机系统作了简介，给出了在超立方体上实现BP算法并进行并行仿真所得到的结论     

神经网络在计算复合材料刚度退化中的应用

复合材料层合板的刚度退化相当复杂,并且是非线性的,目前缺乏足够的理论依据,很难按实际情况计算.本文从神经网络的角度出发,借助于神经网络的自适应性,通过对材料的实际载荷和实测应变值进行学习,使网络掌握其中各层的刚度变化规律.并且通过数值仿真和实验验证,证明此方法是完全可行的.     

一种正交跳频信号动态分选方法

针对复杂电磁环境下正交跳频信号分选时效不高问题,提出了一种正交跳频信号动态分选方法。首先基于滑动窗口的数据流模型,采用构造型神经网络对跳频信号频域数据和方位信息动态聚类,减轻噪声等因素影响,解决方位信息和幅度关联模糊性的问题;再在相应的覆盖簇内运用时频关联方法,实现正交跳频信号的动态分选,实验结果表明该方法是有效的。