基于几何不变性和BP网络的二维目标识别算法

提出了一种基于几何不变性和BP网络的二维目标识别算法.该算法不仅能适应目标物体在旋转、缩放和平移变换(RST变换)下的不变性识别,而且能适应仿射及射影变换下的不变性识别.算法通过对目标物体边缘点进行规格化和对规格化后的边缘点进行5点不变量穷举计算解决了模型图像与目标图像的对应点选取问题;通过将不同观测方位和不同旋转角度的样本图像边缘点的5点不变量集合作为输入向量对BP网络进行训练解决了由于仿射和射影变换造成规格化边缘点间距变化对正确分类的影响.算法不仅能识别多边形而且能识别曲线轮廓的目标物体.      

应用BP网络校正的卡尔曼滤波器

当目标作机动飞行时,由于未知机动引起的估计误差的影响,卡尔曼滤波器的跟踪性能将会严重下降。提出了一种应用BP神经网络对卡尔曼滤波结果进行校正的机动目标跟踪方法,BP网络可以辨识非线性机动,修正卡尔曼估计值,改善跟踪效果。同时还研究了BP网络的自适应算法。对两个变加速机动目标的跟踪结果表明,经过BP网络校正的卡尔曼滤波器不会发生误相关,跟踪的精度较高。     

基于BP神经网络的零件成组技术的研究

提出了一种基于BP神经网络的零件分类方法。通过200个典型零件样本对神经网络进行训练和测试,证明该基于BP神经网络的零件分类成组系统可实现零件的准确分类成组。     

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.     

BTT导弹神经网络直接逆控制算法的FPGA实现研究

针对BTT导弹控制系统中导弹模型的非线性和强耦合的特点,应用逆7系统和神经网络相结合的方法建立了导弹的逆模型.并以俯仰通道为例,设计了逆控制算法的BP神经网络结构,采用Verilog HDL编写了BP神经网络各个功能模块,并将其在FPGA上实现.通过神经网络逆控制算法在FPGA硬件实现和通用计算机上软件实现的对比,表明...     

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.     

基于BP神经网络和飞行参数的航段风险评价

飞行运行风险的定量评估对于民航安全保障具有重要意义。设计并实现了一种基于BP神经网络算法的航段风险评价系统。该系统将大量飞行情景参数直接作为飞行风险评估的基础,通过飞行能力评价体系获得飞行运行风险度经验数据,并使用神经网络实现了任意飞行情景的风险度拟合运算。经过真实航班飞行数据的测试和飞行品质监控系统的验证表明,该系统可为进近着陆阶段任意时刻的飞行运行风险评估提供可靠的结果。     

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.     

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...