偏最小二乘和ANFIS在飞机燃油油量测量中的应用

分析了飞机燃油箱剩余油量测量系统具有变量多、非线性及变量间存在多重相关性的特点,指出单独采用自适应神经模糊推理系统(Adaptive network-based fuzzy inference system．ANFIS)和偏最小二乘(Partial least- squares,PLS)解决飞机燃油箱剩余油量软测量问题均有各自的局限性。结合上述两种方法的优点,提出了一种基于PLS回归与减法聚类的ANFIS软测量模型。将其应用于预测飞机油箱燃油剩余的建模过程中,得到了比采用PLS回归方法更好的精度,从而证明了该建模方法的有效性。     

Motion synchronization in a dual redundant HA/EHA system by using a hybrid integrated intelligent control design

This paper presents an integrated fuzzy controller design approach to synchronize a dis-similar redundant actuation system of a hydraulic actuator (HA) and an electro-hydrostatic actu-ator (EHA) with system uncertainties and disturbances. The motion synchronous control system consists of a trajectory generator, an individual position controller for each actuator, and a fuzzy force tracking controller (FFTC) for both actuators. The trajectory generator provides the desired motion dynamics and designing parameters of the trajectory which are taken according to the dynamic characteristics of the EHA. The position controller consists of a feed-forward controller and a fuzzy position tracking controller (FPTC) and acts as a decoupled controller, improving posi-tion tracking performance with the help of the feed-forward controller and the FPTC. The FFTC acts as a coupled controller and takes into account the inherent coupling effect. The simulation results show that the proposed controller not only eliminates initial force fighting by synchronizing the two actuators, but also improves disturbance rejection performance.     

基于ANFIS的智能导航自适应滤波算法研究

智能导弹等智能化飞行器在快速跨域、高速机动飞行时,由于传感器切换、外形改变等因素,会对组合导航信息融合系统引入随机非Gauss噪声等影响,离线优化的参数往往不能满足滤波器精度的需求。自适应网络模糊推理系统ANFIS是一种将人工神经网络和模糊推理技术相结合而成,符合人类认知特点的决策方法,它可以对导航数据进行学习,实现智能决策、实时修改滤波器内部参数,对滤波器进行优化。仿真结果表明,基于ANFIS优化的智能导航自适应滤波算法可以有效减少噪声和干扰带来的影响,提高导航精度。     

战斗机嵌入式训练系统中的智能虚拟陪练

智能化"实虚"对抗是现代先进战斗机嵌入式训练系统的重要功能需求。自主空战决策控制技术在未来空战装备发展中扮演关键角色。将当前的功能需求和发展中的技术结合起来,得到了空战智能虚拟陪练的概念。先进控制决策技术的引入使得智能虚拟陪练能够帮助飞行员完成复杂的战术训练,而训练中真实的对抗场景为技术的验证提供了理想的环境,大量的训练数据为技术的持续迭代优化提供了保障。作为可学习和进化的空战战术专家,智能陪练在人机对抗和自我对抗中不断优化,当其具备与人相当甚至超越人的战术能力时,可应用于未来的无人空战系统。智能虚拟陪练需要具备4项基本能力：智能决策能力、知识学习能力、对抗自优化能力和参数化表示能力。对其包含的关键技术进行了分析,提出并实现了一个基于模糊推理、神经网络和强化学习的解决方案,展示了其各项基本能力及目前达到的空战水平。未来更多的模型和算法可在智能虚拟陪练的框架中进行验证和优化。     

Data fusion algorithm based on state and attribute parameter

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基于改进的模糊C-Means航迹聚类方法研究

为指导飞行程序的改善和发现管制员的指挥模式,在分析历史飞行航迹特征基础上,应用最小描绘长度(MDL)原理对航迹特征点进行划分,运用融合了遗传算法和模拟退火算法的改进的模糊C-Means算法对特征点进行聚类,通过最长公共子序列(LCS)算法得到航迹相似性矩阵,利用矩阵得到航迹簇,最后形成中心航迹,算例仿真验证了新算法的有效性.     

应用模糊变权的启发式搜索进行飞行轨迹优化

回顾了军机轨迹优化问题的提出及研究历史，介绍了几种主要方法的特点，特别是启发式搜索算法应用于飞行轨迹优化问题时存在的主要问题。提出为了得到较好的近优路径而采取的模糊变权处理方法。通过计算不同加权下解的情况，说明模糊变权方法是使加权随具体情况作相应调整的有效手段，为提高飞行路径规划系统的有效性开辟了一个新思路。     

A salient edges detection algorithm of multi-sensor images and its rapid calculation based on PFCM kernel clustering

Multi-sensor image matching based on salient edges has broad prospect in applications, but it is difficult to extract salient edges of real multi-sensor images with noises fast and accurately by using common algorithms. According to the analysis of the features of salient edges, a novel salient edges detection algorithm and its rapid calculation are proposed based on possibility fuzzy C-means （PFCM） kernel clustering using two-dimensional vectors composed of the values of gray and texture. PFCM clustering can overcome the shortcomings that fuzzy C-means （FCM） cluster- ing is sensitive to noises and possibility C-means （PCM） clustering tends to find identical clusters. On this basis, a method is proposed to improve real-time performance by compressing data sets based on the idea of data reduction in the field of mathematical analysis. In addition, the idea that kernel-space is linearly separable is used to enhance robustness further. Experimental results show that this method extracts salient edges for real multi-sensor images with noises more accurately than the algorithm based on force fields and the FCM algorithm; and the proposed method is on average about 56 times faster than the PFCM algorithm in real time and has better robustness.     

Training effectiveness evaluation of helicopter emergency relief based on virtual simulation

Helicopters are playing an increasingly important part in emergency relief, such as earthquake rescue, firefighting and medical transport. With the development of virtual simulation technology, virtual simulation-based training is widely used in the training of the helicopter crew especially for the dangerous and costly missions mentioned above. A complete training effectiveness evaluation method is proposed to evaluate the trainees’ training effect based on virtual simulation in this paper. A key to this method is regarding the complicated process as a discrete event-activity flow system and establishing the evaluation indicator system. Then expert group and Analytical Network Process (ANP) are applied to determine the weight of indicators. When the training data are processed, there is a novel attempt to apply Fuzzy Comprehensive Evaluation (FCE) model to calculate the two categories of indicators. Eventually, an experiment and the analysis were carried out to validate the evaluation method.     

一种快速的飞行器布局方案优选方法

本文引用系统模糊优选理论与基于数理统计正交表的设计法相结合来处理飞行器布局方案优选问题，并举例说明具体用法，文中方法对整个飞行器方案选择有一定参考价值。