基于人工神经网络与VPMCD的葡萄干等级检测方法研究

为了准确鉴别葡萄干等级,提出一种基于人工神经网络和VPMCD的葡萄干等级检测新方法。以新疆绿无核三个等级的葡萄干作为研究对象,提取颜色、形状的特征参数。采用BP神经网络算法,对比各特征组合对识别率的影响,确定了识别率较高的4个特征参数组合。最后应用VPMCD方法进行样本训练并进行葡萄干等级检测。将提出的方法与SVM、BP神经网络识别结果进行对比分析,结果表明,VPMCD算法识别率达到100%,分级效果明显,运算时间少,识别精度高,为农产品等级检测提供了一个新途径。     

基于神经网络的海面溢油图像分类算法

提出了一种基于Gabor变换、KPCA和神经网络的图像分类方法。首先对图像进行Gabor滤波,获得不同方向的特征参数;然后提取图像的KPCA作为图像的特征,最后利用神经网络进行分类。通过对实验分类结果的定量分析可知,该方法可以获得精度比最小分类模型方法以及最大似然分布模型方法高的分类结果。     

基于视觉的酒液中异物自动识别方法研究

针对白酒酒液中异物特征参数提取与自动判定方法进行了研究。根据酒液检测精度、速度要求,选取合适的图像预处理方法。针对异物的特点提出一种改进的基于轮廓跟踪行程标记算法,在标记的基础上基于单帧图像对各连通区域形态及灰度特征参数进行提取判定,剔除噪声连通区域。在连续帧中提取运动特征参数,最终判定是否为异物连通区域。实验结果表明,本文方法可以简单有效地通过多特征参数提取及融合剔除噪声区域、判别连通区域运动方向,最终识别出酒液中异物。     

低分辨雷达目标识别方法研究

利用目标对对空警戒雷达发射波形的调制效应，采用2种不同的方法，提取出低分辨雷达飞机目标机型（大、小）和飞机目标架次可资分类的特征参数，作为飞机目标机型（大、小）和架次判别的特征向量、特征矢量，然后采用神经网络对目标进行分类识别，给出用BP神经网络进行训练和识别的结果，并在低分辨雷达目标识别样机系统对飞机目标进行分类识别试验中，验证了所提取特征的有效性。表明该方法是有效的，这为常规低分辨雷达空中目标识别提供了一种新的途径。     

基于小波概率网络的局部放电模式识别

针对高压电器局部放电模式分类中样本数较少,常规的分类方法识别率较低,提出了一种基于概率神经网络与小波变换的混合算法。利用实验室模拟的局部放电信号进行小波分解,提取小波能量系数作为特征参数,并作为概率神经网络的输入进行分类。其得到的结果优于多层前馈神经网络及采用顺序最优化学习方法的支持向量机算法。     

基于对象的视频分割算法研究与实现

通过对连续3帧图像进行二次差分，自适应估计差分图像的噪声特征参数并滤除噪声，结合边界信息进行数学形态学运算，最终提取运动目标。实验结果证明，该方法能够从背景不变的图像序列中较好的提取出视频运动对象，具有较强的鲁棒性。     

基于机器视觉的番茄脐腐病的鉴别

利用计算机视觉技术,通过图像处理和模式识别方法,对番茄脐腐病的自动识别进行研究。首先利用中值滤波法对病症图片进行处理,去除噪声,其次采用Otsu算法对病斑进行分割。通过提取病斑区域的颜色特征和形状特征,同时采用贝叶斯判别方法和支持向量机方法实现特征参数的提取。实验结果表明,贝叶斯判别方法对训练样本和实验样本的判别准确率分别达到100%和92%,高于支持向量机方法,实现了番茄脐腐病的准确识别。     

基于CNN特征的宽频段智能测向方法

当前电磁环境日益复杂,利用机器学习方法实现快速且精确的宽频段无线电测向逐渐成为研究的热点。使用卷积神经网络基于端到端的方式完成宽频段测向的方法能够在一定程度上解决宽频段相位模糊的问题,但卷积运算后特征维数大大增加,稀疏的特征影响了最后一层全连接前馈神经网络的分类效果。针对这一问题,提出将无线电测向分为特征学习任务和方向预测任务,使用卷积神经网络作为特征提取器,将通过多层卷积运算得到的结果视为二次提取的特征,作为方向预测任务的输入;针对二次提取特征的稀疏性,提出使用主成分分析算法对特征进行降维,并将稀疏性降低后的特征作为后续分类器的输入。此外,针对特征的特点,探索了几种分类模型作为分类器的效果,包括决策树、随机森林、径向基函数神经网络和K-近邻。实验结果表明,使用主成分分析算法对特征进行降维能够提升训练和测试效率;采用K-近邻构成分类器的准确度明显高于原卷积神经网络的准确度;若需要兼顾准确度和测向效率,采用随机森林构成分类器的效果最好。     

基于相空间重构实现非线性语音清浊音判决

以相空间重构理论为基础,采用Takens定理重构语音信号相空间并提取相似序列重复度(RPT)特征参数。利用清浊音RPT参数的差异,提出并实现了一种采用BP神经网络进行非线性清浊音判决的方法,得到了明显优于传统算法的结果。本文方法为语音特征提取和识别研究提供了新的途径     

基于自编码器的雷达信号联合预分选方法

将脉内特征提取、调制类型识别和聚类分选联合,提出了一种基于自编码器的雷达信号联合预分选方法。现有的基于脉内特征的聚类预分选方法需要预先设计特征参数提取方法,而所提方法可自动提取脉内特征参数,并根据聚类结果,对提取的特征参数进行调整,从而改变了以往分选算法的单向流程,引入了反馈机制以深入挖掘特征信息。仿真结果表明,该方法能在低信噪比环境下对雷达信号的脉内特征进行提取,并依靠脉内特征参数进行分选。     

基于SVM实现人眼注视与否的探知

采用基于统计学习理论的支持向量机(SVM,support vector machine)方法对人眼注视与否进行探知。根据结构风险最小化(SRM,structural risk minimization)准则,在最小化已知样本点误差的同时,尽量缩小模型预测误差的上界,改善了模型的泛化能力。实验结果显示,在训练样本数有限的情况下,学习后模型对测试样本的正确识别率达到100％,比此前采用其它方法所获得的识别结果识别率更高,训练及识别过程速度更快,基本上能够满足实时性要求,也更接近人类视觉对注视与否的探知的特点。     

基于主成分分析和支持向量机的人眼注视识别

眼睛注视是人类传播信息的一个重要的媒介,实现对人眼注视与否的探知已经成为人机交互领域中一个亟需解决的问题。针对这个问题,首先采用主成分分析方法对人眼图像进行预处理,得到有利于人眼分类的一组低维特征系数,然后采用基于统计学习理论的支持向量机网络实现对人眼注视与否的判断,并通过大量的实验得出适合于人眼注视与否的最佳主成分特征维数。实验表明,在有限个样本的训练下,当PCA特征维数在18至23之间时,能够得到令人满意的结果,正确识别率均在9l％之上,而特征维数为20时,测试样本被正确识别的比率最高,可达98．78％。     

一种合成孔径雷达对地面运动目标成像和精确定位的算法

该文针对正侧视合成孔径雷达工作体制下运动目标成像、定位问题进行研究。分析了运动目标成像与静止目标成像的异同。提出利用距离历程拟合,结合静物场景成像的部分已知信息(地面道路或者桥梁的方向),准确估计t=0时刻目标的坐标和两维速度,不仅为运动目标精确成像提供了信息,同时得到目标运动轨迹相对地面场景的准确位置。该文提出的方法对于目标在照射孔径内的位置没有特殊假设(以往研究的时频分析方法需要假设目标在t=0时刻位于载机的正侧方向)。最后定量分析了不同形式匹配滤波器对运动目标回波成像的效果以及采用静止目标匹配滤波器对运动目标回波进行成像处理而产生的移位和散焦影响.     

Detection, classification, and superposition resolution of actionpotentials in multiunit single-channel recordings by an on-linereal-time neural network

Determination of single-unit spike trains from multiunit recordings obtained during extracellular recording has been the focus of many studies over the last two decades. In multiunit recordings, superpositions can occur with high frequency if the firing rates of the neurons are high or correlated, making superposition resolution imperative for accurate spike train determination. In this work, a connectionist neural network (NN) was applied to the spike sorting challenge. A novel training scheme was developed which enabled the NN to resolve some superpositions using single-channel recordings. Simulated multiunit spike trains were constructed from templates and noise segments that were extracted from real extracellular recordings. The simulations were used to determine the performances of the NN and a simple matched template filter (MTF), which was used as a basis for comparison. The network performed as well as the MTF in identifying nonoverlapping spikes, and was significantly better in resolving superpositions and rejecting noise. An on-line, real-time implementation of the NN discriminator, using a high-speed digital signal processor mounted inside an IBM-PC, is now in use in six laboratories     

基于最近邻-拓扑图的异类传感器目标关联算法

针对雷达和高动态平台上的红外传感器构成的异类传感器信息融合系统中的目标关联问题,该文提出了一种基于最近邻-拓扑图的目标关联算法。该算法避免了系统误差补偿环节,有效地克服了最近邻方法对系统偏差敏感和拓扑图方法运算量大的不足,显著提高了存在系统误差条件下的关联成功率,且具有很强的稳健性。数值实验结果表明了该方法的有效性,目标关联正确率在90%以上。      

Using neural networks and genetic algorithms to enhance performance in an electronic nose

Sensitivity, repeatability, and discernment are three major issues in any classification problem. In this study, an electronic nose with an array of 32 sensors was used to classify a range of odorous substances. The collective time response of the sensor array was first partitioned into four time segments, using four smooth time-windowing functions. The dimension of the data associated with each time segment was then reduced by applying the Karhunen-Loéve (truncated) expansion (KLE). An ensemble of the reduced data patterns was then used to train a neural network (NN) using the Levenberg-Marquardt (LM) learning method. A genetic algorithm (GA)-based evolutionary computation method was used to devise the appropriate NN training parameters, as well as the effective database partitions/features. Finally, it was shown that a GA-supervised NN system (GANN) outperforms the NN-only classifier, for the classes of the odorants investigated in this study (fragrances, hog farm air, and soft beverages).     

Design and implementation of industrial neural network controller using backstepping

In this paper, a novel neural network (NN) backstepping controller is modified for application to an industrial motor drive system. A control system structure and NN tuning algorithms are presented that are shown to guarantee stability and performance of the closed-loop system. The NN backstepping controller is implemented on an actual motor drive system using a two-PC control system developed at The University of Texas at Arlington. The implementation results show that the NN backstepping controller is highly effective in controlling the industrial motor drive system. It is also shown that the NN controller gives better results on actual systems than a standard backstepping controller developed assuming full knowledge of the dynamics. Moreover, the NN controller does not require the linear-in-the-parameters assumption or the computation of regression matrices required by standard backstepping.     

基于独立成份分析和支持向量机的人眼注视研究

采用独立成份分析(ICA)方法对单眼宽约为40 pixel的低分辨率人眼图像进行预处理,并把得到的一组组合系数输入支持向量机(SVM)判断图像中人眼的注视与否.对于单用户的注视探测,一维组合系数输入实现了100%的正确识别率;对于多用户的注视探测,二维组合系数输入实现了100%的正确率识别率.     

A neural-network technique for the retrieval of atmospheric temperature and moisture profiles from high spectral resolution sounding data

A novel statistical method for the retrieval of atmospheric temperature and moisture profiles has been developed and evaluated with simulated clear-air and observed partially cloudy sounding data from the Atmospheric InfraRed Sounder (AIRS) and the Advanced Microwave Sounding Unit (AMSU). The algorithm is implemented in two stages. First, a projected principal components (PPC) transform is used to reduce the dimensionality of and optimally extract geophysical profile information from the cloud-cleared infrared radiance data. Second, a multilayer feedforward neural network (NN) is used to estimate the desired geophysical parameters from the PPCs. For the first time, NN temperature and moisture retrievals are presented using actual microwave and hyperspectral infrared observations of cloudy atmospheres, over both ocean and land (with variable terrain elevation), and at all sensor scan angles. The performance of the NN retrieval method (henceforth referred to as the PPC/NN method) was evaluated using global Earth Observing System Aqua orbits colocated with European Center for Medium-range Weather Forecasting fields for seven days throughout 2002 and 2003. Over 350,000 partially cloudy footprints were used in the study, and retrieval performance was compared with the AIRS Science Team Level-2 retrieval algorithm (version 3). Performance compares favorably with that obtained with simulated clear-air observations from the NOAA88b radiosonde set of approximately 7500 profiles. The PPC/NN method requires significantly less computation than traditional variational retrieval methods, while achieving comparable performance.     

分布式目标参数估计的波束域处理方法

分布式目标参数估计的阵元域处理方法的运算量较大,将波束域处理方法用于分布式目标参数估计,降低分布式目标高分辨测向算法的运算量,提高输入信噪比,同时降低算法对系统误差的敏感性.在分布式目标源比较集中的情况下,利用较少的几个波束形成波束空间进行参数估计,能实现良好的估计性能.仿真结果验证了此方法的有效性.