一种基于免疫的监督式分类算法

人工免疫识别系统（AIRS）已被证实为一种高效的分类器,并成功应用于模式识别等领域。然而AJRS存在的记忆细胞数目庞大、分类准确率低等缺陷,限制了进一步的应用。为克服这些缺陷,提出了一种基于免疫的监督式分类算法（AIUC）。AIUC首先初始化记忆细胞;然后通过对每一个训练抗原的学习,进行B细胞进化,在B细胞收敛后,优选出最佳的B细胞对记忆细胞进行更新;最后通过记忆细胞对测试数据进行kNN分类。就数据集I-ris、Ionosphere,、Diabetes和Sonar分别进行的对比实验结果表明,AIUC比AIRS记忆细胞分别减小了5．6％、18％、19．6％和31％,分类准确率提高到98．2％、96．9％、78．3％和92．3％。该算法具有非线性,以及克隆选择、免疫网络和免疫记忆等生物免疫系统特征,可更好地应用于模式识别、异常检测等领域。     

浅谈吹扫--GC／MS联用技术测定水中挥发性有机物

本文根据《地表水环境质量标准》（GB3838—2002）中的表3一集中式生活饮用水地表水源地中所列氯乙烯、三氯甲烷、四氯化碳等26个挥发性有机物的分析特点，将其加以归并，建立了采用吹扫捕集-气相色谱，质谱联用技术进行分析的方法，分析结果表明本方法所分析的26种目标化合物的回收率范围为88．5％～117％，精密度RSD％范围为0．71％-8．50％，检测限范围为O．10—0．61ug，L之间，均满足地表水中挥发性有机物的分析要求。     

多维线性模型参数估计的神经网络方法

本文提出一种用于多维线性模型（ＡＲ，ＡＲＭＡ）参数估计的神经网络方法和相应的递归预测误差算法。本文在分析多输入、单输出，含一个隐含和多层神经网络的输入输出关系的基础上，提出了首先将理想输出Ｘｉ进行预畸变（Ｆ（Ｘｔ））作为神经网络的训练目标。当神经网络训练完毕后，网络的连接权就是待估计的线性模型参数。本文提出方法的优点是网络结构简单，估计结果准确。仿真模拟结果表明，本文所提出的神经网络方法估计多维线性模型参数是有效的。     

基于径向基函数神经网络的滚动轴承故障模式的识别

径向基函数（RBF）神经网络是一种3层前馈性神经网络，它具有较强的函数逼近能力和分类能力。鉴于径向基函数神经网络的优点，在对滚动轴承振动信号特征分析的基础上，提出了采用时序方法对其建立AR模型，利用AR模型参数建立径向基函数神经网络，并用该网络对滚动轴承的故障模式进行了识别。理论和试验证明了该方法的有效性，且具有较高的识别精度。     

基于神经网络的离散变结构控制系统

系统研究了基于神经网络的离散变结构控制系统设计方法，提出了几种具体设计方案．神经网络的引入可以使滑模（变结构）控制具备学习与自适应能力，使控制信号得以柔化，从而能够减轻或避免困扰常规滑模控制器的抖振现象，改善控制效果．     

基于粗集理论和神经网络的数据融合

提出了一种基于粗集理论和神经网络结合的数据融合方法。利用粗集对输入信息进行约简，剔除冗余信息，简化了神经网络模型，提高了训练速度，进而提高整个融合系统的速度。并通过仿真实验证明了它的可行性。     

基于粗糙集和神经网络的联合模式识别系统

提出了一种划分属性离散区间的新方法．针对这种划分，提出一种约简和去噪的方法．随后，建立了粗糙集和LVQ神经网络的联合模式识别系统．最后，比较了用该系统和仅用神经网络进行识别的效果，证明了该方法的有效性．     

ICP—AES法测定钛合金中微量钇

对ＩＣＰ－ＡＥＳ法测定钛合金中微量钇的技术做了研究提出了一种新的测试程序，其测定范围ｗ（Ｙ）＝０．０５％－０．００２５％，相对误差≤±１０％，相对标准偏差≤５％。方法简便快速，结果令人满意。     

基于YOLOv3-spp的汽车轮毂表面缺陷检测算法研究与分析

针对传统人工检测方法效率低且准确率不高等问题，提出一种基于YOLOv3-spp网络的自动缺陷检测方法。首先通过图像切片提取缺陷区域，然后将提取的缺陷图片经过数据增强后组成数据集并以此训练YOLOv3-spp网络，接着对比分析了不同深度学习网络及数据集筛选方法对轮毂表面缺陷的检测效果。实验结果表明：在工业现场采集的数据集上，训练好的YOLOv3-spp神经网络可以准确地定位，并识别出点状、线性、油泥油漆、针孔4类缺陷，其平均准确率分别为84.5%、93.4%、95.4%和89.5%,检测速度达到35 ms/幅，满足检测的实时性要求，且检测准确率优于Faster R-CNN和SSD两种常用神经网络。     

（Na1/2Bi1/2）TiO3无铅压电陶瓷的复合离子与补偿电价取代效应研究

采用传统陶瓷制备方法，制备出两种钙钛矿结构无铅新压电陶瓷材料（1-x）（Na1/2Bi1/2）TiO3-x（Na1/2Bi1/2）（Sb1/2Nb1/2）O3和（1-y）（Na1/2Bi1/2）TiO3-yBi（Mg2/3Nb1/3）O3。研究了复合离子与补偿电价取代对（Na1/2Bi1/2）TiO3陶瓷晶体结构和压电性能的影响。）（射线衍射分析表明，在所研究的组成范围内两种陶瓷材料均能够形成纯钙钛矿固溶体．陶瓷材料的介电常数-温度曲线显示两种陶瓷体系具有明显的弛豫铁电体特征．适量的复合离子与补偿电价取代都能提高材料的压电性能，在工=0．8％时，陶瓷的压电常数d33=97pC/N，厚度机电耦合系数kr=0．50，在y=0．7％时d33=94pC/N，y=0．9％时k1=0．46，为所研究组成中的最大值。两种陶瓷体系都具有较大的‰值和较小的kp值，具有较大的各向异性，是一种优良的、适合高频下使用的超声换能材料．     

Predicting motor vehicle collisions using Bayesian neural network models: an empirical analysis

Statistical models have frequently been used in highway safety studies. They can be utilized for various purposes, including establishing relationships between variables, screening covariates and predicting values. Generalized linear models (GLM) and hierarchical Bayes models (HBM) have been the most common types of model favored by transportation safety analysts. Over the last few years, researchers have proposed the back-propagation neural network (BPNN) model for modeling the phenomenon under study. Compared to GLMs and HBMs, BPNNs have received much less attention in highway safety modeling. The reasons are attributed to the complexity for estimating this kind of model as well as the problem related to "over-fitting" the data. To circumvent the latter problem, some statisticians have proposed the use of Bayesian neural network (BNN) models. These models have been shown to perform better than BPNN models while at the same time reducing the difficulty associated with over-fitting the data. The objective of this study is to evaluate the application of BNN models for predicting motor vehicle crashes. To accomplish this objective, a series of models was estimated using data collected on rural frontage roads in Texas. Three types of models were compared: BPNN, BNN and the negative binomial (NB) regression models. The results of this study show that in general both types of neural network models perform better than the NB regression model in terms of data prediction. Although the BPNN model can occasionally provide better or approximately equivalent prediction performance compared to the BNN model, in most cases its prediction performance is worse than the BNN model. In addition, the data fitting performance of the BPNN model is consistently worse than the BNN model, which suggests that the BNN model has better generalization abilities than the BPNN model and can effectively alleviate the over-fitting problem without significantly compromising the nonlinear approximation ability. The results also show that BNNs could be used for other useful analyses in highway safety, including the development of accident modification factors and for improving the prediction capabilities for evaluating different highway design alternatives.     

Artificial neural networks for the automated detection of trichloroethylene by passive Fourier transform infrared spectrometry

Artificial neural networks are applied to the automated classification of trichloroethylene (TCE) signatures from passive Fourier transform infrared remote sensing interferogram data. Through the use of three data collection methods, a combination of laboratory and field data is acquired that allows the methodology to be evaluated under a variety of infrared background conditions and in the presence of potentially interfering compounds such as sulfur hexafluoride, methyl ethyl ketone, acetone, carbon tetrachloride, and ammonia. To maximize the computational efficiency of the network optimization, experimental design techniques are employed to develop a training protocol for the network that takes into account the relationships among five variables that are related either to the network architecture or to the training process. This protocol is implemented for the case of a back-propagation neural network (BNN) and is used to develop an optimized network for the detection of TCE. The classification performance of the network is assessed by comparing both TCE detection capabilities and false detection rates to similar classification results obtained with the technique of piecewise linear discriminant analysis (PLDA). When applied to prediction data withheld from the optimization of both the BNN and PLDA algorithms, the BNN method is observed to outperform PLDA overall, with TCE detection rates in excess of 99% and false detection rates less than 0.5%.     

BP神经网络在复合材料研究中的应用

人工神经网络因能处理复杂的非线性问题而成为材料科学研究的一种重要方法.在介绍BP神经网络的基础上,综述了其在复合材料设计、工艺优化、性能预测、损伤检测及预测等方面的应用情况,分析了应用中存在的问题,展望了其发展趋势.     

A Hybrid Neural Network and Box-Jenkins Models for Time Series Forecasting

Time series forecasting plays a significant role in numerous applications, including but not limited to, industrial planning, water consumption, medical domains, exchange rates and consumer price index. The main problem is insufficient forecasting accuracy. The present study proposes a hybrid forecasting methods to address this need. The proposed method includes three models. The first model is based on the autoregressive integrated moving average (ARIMA) statistical model; the second model is a back propagation neural network (BPNN) with adaptive slope and momentum parameters; and the third model is a hybridization between ARIMA and BPNN (ARIMA/BPNN) and artificial neural networks and ARIMA (ARIMA/ANN) to gain the benefits of linear and nonlinear modeling. The forecasting models proposed in this study are used to predict the indices of the consumer price index (CPI), and predict the expected number of cancer patients in the Ibb Province in Yemen. Statistical standard measures used to evaluate the proposed method include (i) mean square error, (ii) mean absolute error, (iii) root mean square error, and (iv) mean absolute percentage error. Based on the computational results, the improvement rate of forecasting the CPI dataset was 5%, 71%, and 4% for ARIMA/BPNN model, ARIMA/ANN model, and BPNN model respectively; while the result for cancer patients’ dataset was 7%, 200%, and 19% for ARIMA/BPNN model, ARIMA/ANN model, and BPNN model respectively. Therefore, it is obvious that the proposed method reduced the randomness degree, and the alterations affected the time series with data non-linearity. The ARIMA/ANN model outperformed each of its components when it was applied separately in terms of increasing the accuracy of forecasting and decreasing the overall errors of forecasting.

     

Mean Opinion Score Estimation for Mobile Broadband Networks Using Bayesian Networks

Mobile broadband (MBB) networks are expanding rapidly to deliver higher data speeds. The fifth-generation cellular network promises enhanced-MBB with high-speed data rates, low power connectivity, and ultra-low latency video streaming. However, existing cellular networks are unable to perform well due to high latency and low bandwidth, which degrades the performance of various applications. As a result, monitoring and evaluation of the performance of these network-supported services is critical. Mobile network providers optimize and monitor their network performance to ensure the highest quality of service to their end-users. This paper proposes a Bayesian model to estimate the minimum opinion score (MOS) of video streaming services for any particular cellular network. The MOS is the most commonly used metric to assess the quality of experience. The proposed Bayesian model consists of several input data, namely, round-trip time, stalling load, and bite rates. It was examined and evaluated using several test data sizes with various performance metrics. Simulation results show the proposed Bayesian network achieved higher accuracy overall test data sizes than a neural network. The proposed Bayesian network obtained a remarkable overall accuracy of 90.36% and outperformed the neural network.     

实现CDMA最佳多用户检测的神经网络方法

提出并讨论了两种实现码分多址系统中最佳多用户检测的神经网络方法。一种基于离散Ｈｏｐｆｉｅｌｄ神经网络，另一种基于采用反向传播算法的多层感知器神经网络。理论分析和计算机模拟都证实了这两种神经网络方法的可行性，优越性和实用性。前者适用“固定”用户情况；后者既可用于“固定”用户吼适用于移动用户的情况，因而在未来的ＣＤＭＡ个人通信网中有较好的应用前景。     

Denoising by coupled partial differential equations and extracting phase by backpropagation neural networks for electronic speckle pattern interferometry

We extend and refine previous work [Appl. Opt. 46, 2907 (2007)]. Combining the coupled nonlinear partial differential equations (PDEs) denoising model with the ordinary differential equations enhancement method, we propose the new denoising and enhancing model for electronic speckle pattern interferometry (ESPI) fringe patterns. Meanwhile, we propose the backpropagation neural networks (BPNN) method to obtain unwrapped phase values based on a skeleton map instead of traditional interpolations. We test the introduced methods on the computer-simulated speckle ESPI fringe patterns and experimentally obtained fringe pattern, respectively. The experimental results show that the coupled nonlinear PDEs denoising model is capable of effectively removing noise, and the unwrapped phase values obtained by the BPNN method are much more accurate than those obtained by the well-known traditional interpolation. In addition, the accuracy of the BPNN method is adjustable by changing the parameters of networks such as the number of neurons.     

多联机系统制热工况下的制冷剂充注量故障诊断模型——基于多层卷积神经网络

本文提出一种基于卷积神经网络的故障诊断模型,并通过正交试验优化了3层网络的卷积核和神经元数目,利用图形化的多联机(VRF)系统制冷剂充注量故障实验数据训练了多层卷积神经网络,评估了本模型的故障诊断性能。结果表明:该"数据图形化-多层卷积神经网络"方法建立的模型能够有效进行多联机制冷剂充注量故障诊断,20个输入特征时,对9类故障诊断总正确率最大为91%,比传统BP神经网络达到更高的诊断精度。该方法首次利用卷积神经网络完成了VRF制冷剂充注量故障诊断,为相关研究的拓展奠定了基础。     

Neural Network Burst Pressure Prediction in Composite Overwrapped Pressure Vessels Using Mathematically Modeled Acoustic Emission Failure Mechanism Data

The purpose of this research was to predict burst pressures in composite overwrapped pressure vessels (COPVs) by using mathematically modeled acoustic emission (AE) data. Both backpropagation neural network (BPNN) and multiple linear regression (MLR) analyses were performed on various subsets of the low proof pressure AE data to predict burst pressures and to determine if the two methods were comparable. AE data were collected during hydrostatic burst testing on the 15-inch diameter COPVs. Once collected, the AE data were filtered to eliminate noise then classified into AE failure mechanism data using a MATLAB Kohonen self-organizing map (SOM). The matrix cracking only amplitude distribution data were mathematically modeled using bounded Johnson distributions with the four Johnson distribution parameters – ?, λ, γ, and η – employed as inputs to make both the BPNN and MLR predictions. The burst pressure predictions generated using a MATLAB BPNN resulted in a worst case error of 1.997% as compared to ?1.666% for the MLR analysis, suggesting comparability. However, the MLR analysis required the data from all nine COPVs to get approximately the same results as the BPNN training on just five COPVs; plus, MLR analyses are intolerant to noise, whereas BPNNs are not.     

基于神经网络的塑料打包带高光谱模式识别

目的 建立一种快速、准确、无损的塑料打包带的检验及分类方法。方法 利用高光谱在波长为350～990 nm的条件下采集52个不同来源的塑料打包带样品的高光谱数据,并对样品进行Savitzky-Golay平滑处理,同时结合主成分分析对样品进行降维。将提取到的主成分进行K-Means聚类,以聚类结果为依据建立径向基函数神经网络（RBFNN）与BP神经网络模型（BPNN）。结果 打包带样品的高光谱谱图在400～500 nm、600～700 nm处有较大区别。实验共提取了5个初始特征值大于1的主成分,可以解释96.633%的原始数据。通过K-means聚类将塑料打包带样品分为6类,Calinski-Harabasz指数为28.76,RBFNN分类准确率为86.7%;BPNN分类准确率为98.1%,BPNN的分类效果更好。结论 研究表明神经网络在高光谱谱图分类处理上具有较高的准确度,同时也验证了高光谱在区分检验塑料打包带类物证的可行性与科学性,为公安机关提供了一种新的检验方法。