基于模糊神经网络的涡结构识别方法研究

研究气动光学传输效应产生的机理是红外成像末制导的共性基础技术之一,基于涡结构对光学传输效应进行建模是一种非常有效的方法,而涡结构的识别是其必要前提。文中提出一种新的涡结构识别方法,把折射率场经小波变换后的系数矩阵等效为具有一定纹理结构的图像,计算图像的共生矩阵及其统计量,由于涡结构模式复杂,特征量较多,设计了等价结构的模糊神经网络进行涡结构识别。与小波分解后直接提取特征量的识别方法相比,本文的方法从空、频角度更加准确全面地表征湍流涡结构模式,计算机仿真结果表明该方法优于神经网络的识别效率。     

竞争学习模糊神经网络及在导弹故障诊断中的应用

尽管基于解析冗余的故障诊断方法有许多突出的优点而越来越多地得到的研究和应用，但它依赖于系统的模型，当系统存在非线性或不确定性时，存在难以建模的困难，模糊神经网络可以通过学习建立系统的模型，且模型参数有明确的物理意义，初始参数易于选择，成为解决这一问题的优选途径，作者通过把模糊神经网络的学习转化为竞争聚类和线性优化问题，基于竞争聚类和最小二乘原理，提出了一种模糊神经网络学习算法，并在某伺服机构上进行     

Aircraft robust multidisciplinary design optimization methodology based on fuzzy preference function

This paper presents a Fuzzy Preference Function-based Robust Multidisciplinary Design Optimization (FPF-RMDO) methodology. This method is an effective approach to multidisciplinary systems, which can be used to designer experiences during the design optimization process by fuzzy preference functions. In this study, two optimizations are done for Predator MQ-1 Unmanned Aerial Vehicle (UAV): (A) deterministic optimization and (B) robust optimization. In both problems, minimization of takeoff weight and drag is considered as objective functions, which have been optimized using Non-dominated Sorting Genetic Algorithm (NSGA). In the robust design optimization, cruise altitude and velocity are considered as uncertainties that are modeled by the Monte Carlo Simulation (MCS) method. Aerodynamics, stability and control, mass properties, performance, and center of gravity are used for multidisciplinary analysis. Robust design optimization results show 46% and 42% robustness improvement for takeoff weight and cruise drag relative to optimal design respectively.     

Prediction of aerodynamic characteristics of an aircraft model with and without winglet using fuzzy logic technique

This paper describes the potentials of an aircraft model without and with winglet attached with NACA wing No. 65-3-218. Based on the longitudinal aerodynamic characteristics analyzing for the aircraft model tested in low subsonic wind tunnel, the lift coefficient (CL) and drag coefficient (CD) were investigated respectively. Wind tunnel test results were obtained for CL and CD versus the angle of attack α for three Reynolds numbers Re (1.7×¹⁰⁵, 2.1×¹⁰⁵, and 2.5×¹⁰⁵) and three configurations (configuration 1: without winglet, configuration 2: winglet at 0° and configuration 3: winglet at 60°). Compared with conventional technique, fuzzy logic technique is more efficient for the representation, manipulation and utilization. Therefore, the primary purpose of this work was to investigate the relationship between lift coefficients and drag coefficients with free-stream velocities and angle of attacks, and to illustrate how fuzzy expert system (FES) might play an important role in prediction of aerodynamic characteristics of an aircraft model with the addition of winglet. In this paper, an FES model was developed to predict the lift and drag coefficients of the aircraft model with winglet at 60°. The mean relative error of measured and predicted values (from FES model) were 6.52% for lift coefficient and 4.74% for drag coefficient. For all parameters, the relative error of predicted values was found to be less than the acceptable limits (10%). The goodness of fit of prediction (from FES model) values were found as 0.94 for lift coefficient and 0.98 for drag coefficient which were close to 1.0 as expected.     

基于聚类模糊系统的动态数据野值剔除方法

针对影响遥测参数处理和分析的野值问题,提出了基于聚类法实时设计模糊系统实现动态数据野值辨识和剔除的新方法。该方法能够自适应跟踪不同变化特性的遥测参数,基于聚类法实现模糊系统的动态建模并获得预测值与观测值的残差序列,再按照狄克松准则实现野值的快速剔除。对实测数据的仿真实验表明：该方法能够显著降低动态建模的复杂度,快速跟踪信号变化,方法可行且有效。     

飞控系统故障检测的参数模糊自适应调节

提出通过模型参数的模糊比例积分自适应调节，以校正基于模型的飞控系统故障检测中的系统数学模型偏差。针对模型中误差差较大的迎角导数值，将俯仰角加速度的测量值与教学模型估计值之差作为比例积分调节的反馈量，动态实时校正数学模型参数。     

自由飞行空间机器人基座位姿调整的模糊PD控制

由于自由飞行空间机器人处于微重力的环境,因此其在目标获取的过程中必然会出现因机械臂运动和环境影响而导致基座的复杂运动.本文设计了自由飞行空间机器人位姿调整的模糊PD算法,根据接收到的实时位置信息,空间机器人可以自主地调整基座的姿态和位置来校正位姿误差,保证目标的稳定捕获.利用开发的实验平台与传统的on/off式控制器进行了实验对比,验证了此方法在稳定性、控制精度和燃料消耗上都有较好的表现.     

Segmentation of extreme ultraviolet solar images via multichannel fuzzy clustering

The study of the variability of the solar corona and the monitoring of its traditional regions (Coronal Holes, Quiet Sun and Active Regions) are of great importance in astrophysics as well as in view of the Space Weather and Space Climate applications. Here we propose a multichannel unsupervised spatially constrained fuzzy clustering algorithm that automatically segments EUV solar images into Coronal Holes, Quiet Sun and Active Regions. Fuzzy logic allows to manage the various noises present in the images and the imprecision in the definition of the above regions. The process is fast and automatic. It is applied to SoHO–EIT images taken from February 1997 till May 2005, i.e. along almost a full solar cycle. Results in terms of areas and intensity estimations are consistent with previous knowledge. The method reveal the rotational and other mid-term periodicities in the extracted time series across solar cycle 23. Further, such an approach paves the way to bridging observations between spatially resolved data from imaging telescopes and time series from radiometers. Time series resulting form the segmentation of EUV coronal images can indeed provide an essential component in the process of reconstructing the solar spectrum.     

基于加工能力模糊聚类的设备分组方法

为了在工艺设计中高效、合理、准确地确定加工设备,实现资源优化配置,提出了基于加工能力模糊聚类的设备分组方法.该方法采用加工能力元描述设备的加工能力,通过对加工能力元的聚类分组,实现对设备的分组.在此基础上,建立了加工能力元的数学模型,采用区间数模糊C-均值聚类算法对模型进行了求解,并采用改进的最大最小距离算法产生初始分组中心.最后,通过具体实例,验证了该方法的有效性.     

Crop parameters estimation by fuzzy inference system using X-band scatterometer data

Learning fuzzy rule based systems with microwave remote sensing can lead to very useful applications in solving several problems in the field of agriculture. Fuzzy logic provides a simple way to arrive at a definite conclusion based upon imprecise, ambiguous, vague, noisy or missing input information. In the present paper, a subtractive based fuzzy inference system is introduced to estimate the potato crop parameters like biomass, leaf area index, plant height and soil moisture. Scattering coefficient for HH- and VV-polarizations were used as an input in the Fuzzy network. The plant height, biomass, and leaf area index of potato crop and soil moisture measured at its various growth stages were used as the target variables during the training and validation of the network. The estimated values of crop/soil parameters by this methodology are much closer to the experimental values. The present work confirms the estimation abilities of fuzzy subtractive clustering in potato crop parameters estimation. This technique may be useful for the other crops cultivated over regional or continental level.