一种用于测量系统信息处理的神经网络设计

介绍了一种在测量系统中使用的基于递推预报误差算法的前馈神经网络的设计以及实现方法。将该网络应用于测量系统模型的仿真试验取得了良好的效果。文中给出了试验的结果 ,并对该网络的应用进行了讨论     

网络处理器及其在第三层转发中的应用

首先回顾了网络处理器出现的背景,并以Intel公司的IXP1200网络芯片为例子,简单介绍了该芯片的硬件结构和特点。最后分析了该芯片在第三层数据转发中应用的三个试验,并对试验的结果进行了简单分析。     

一种飞行试验AFDX航电系统的采集网络时延测试方法

全双工交换以太网AFDX正逐渐取代传统的1553B,成为新一代航电系统的构架协议;AFDX总线具有以太网的时延特点;飞行试验对航电系统的采集网络数据时间延迟测试有特殊的需求,针对飞行试验测试的特点,提出了一种飞行试验AFDX航电系统的采集网络时延测试方法,解决了航电AFDX采集网络时延测试难题;通过对某试验机的AFDX采集网络时间延迟测试,提高了飞行试验某AFDX航电子系统的试验计算精度,证明了该方法在工程中的有效性.     

一种飞行试验FC航电系统的采集网络时延测试方法

针对飞行试验FC总线测试的特点,文章提出了一种飞行试验FC航电系统的采集网络时延测试方法,在不影响原航电系统信息传输结构与特性的前提下,测试到通信消息从航电子系统端口发出的精准时刻,解决了航电FC采集网络时延测试难题;通过对某试验机的FC采集网络时间延迟测试,提高了飞行试验某航电子系统的试验计算精度,证明了该方法在工程中的有效性。     

网络处理器及其在第三层转发中的应用

首先回顾了网络处理器出现的背景,并以Intel公司的IXP1200网络芯片为例子,简单介绍了该芯片的硬件结构和特点.最后分析了该芯片在第三层数据转发中应用的三个试验,并对试验的结果进行了简单分析.     

基于以太网的MANET网络试验床

在移动自组织网络（MANET）的研究中,一般采用软件仿真和现场试验的方式对所设计的路由协议进行验证。软件仿真常用来对路由协议的原理和性能进行初步验证,现场试验则常用在开发过程中的测试和产品上线之前的最终验证。在MANET的路由协议开发过程中采用现场试验的方式进行测试其结果虽然准确可靠,但是试验成本较高、操作相对较为复杂,并且试验结果容易受到地理环境等外界因素的影响。针对上述情况,本文提出一种基于有线以太网构建MANET网络试验床的方法,并基于VxWorks实时操作系统,设计和实现一个接近于真实试验环境的MANET网络试验床。该试验床对标准的TCP/IP协议栈进行扩展,在网络层和数据链路层中间添加模拟无线链路层,从而在有线链路上模拟节点移动、无线信道通信等MANET网络特性。此外,试验床还可以通过管控平台配置不同测试场景,并可以实时采集网络协议栈的各层试验数据,从而方便网络研究人员进行测试和验证。该试验床克服了现场试验成本高、操作复杂等缺点,解决了现场试验过程中很难对问题场景进行重现的问题,明显减少了试验时间,提高了开发效率。     

网络信息审计系统中数据采集的研究与实现

数据采集是网络信息审计系统的基础组件.故而对流行的网络数据采集工具Libpcap进行了详细的分析,指出该工具只适合在普通网络环境下运行,不能满足基于高速网络的信息审计系统的需求.为此,对零拷贝技术进行了研究与试验,并成功实现了该技术,从软件上满足了基于高速网络的信息审计系统的需求.     

基于网络靶场技术的安全检测方法设计

为加强电力工控系统安全，研究了基于网络靶场技术的电力工控系统安全检测方法。通过在网络靶场平台上构建1个电力工控系统，模拟仿真试验环境。利用漏洞扫描、安全分析和攻击仿真等技术，对电力工控系统进行全面深入的安全检测，以识别并修复系统中的漏洞、提高系统安全性。通过分析电力工控系统的特点和安全威胁，介绍了基于网络靶场技术的安全检测方法，并设计试验进行验证。试验结果表明，该方法在检测电力工控系统安全性方面具有较高的有效性。该方法创新性地使用了网络靶场技术来检测电力工控系统的安全隐患，较好地解决了电力工控系统在线检测或手工检测的局限性问题。     

基于可靠链路层模型的节能路由协议

针对不可靠的Ad Hoc网络,提出一种基于可靠链路层的节能路由协议。该路由协议在寻路过程中,综合考虑了最大化网络生命期和最小化每个包的能量花费。而且,该协议考虑了链路的可靠性,采用了功率控制技术,能够恰当处理包丢失问题。通过大量仿真试验,并与已有的路由协议进行对比,表明该协议在保证网络性能的前提下能有效节省能量,、延长网络生命期。     

一种基于CORBA的分布式智能网络管理系统

针对集中式网络管理系统在智能性和时效性上的局限性,该文提出了一种基于CORBA的分布式智能网络管理系统模型,并进行了理论分析和仿真实验;试验结果表明,该系统可以有效地减少大规模网络的带宽消耗,提高了网络的管理效率。     

Approximating support vector machine with artificial neural network for fast prediction

Support vector machine (SVM) is a powerful algorithm for classification and regression problems and is widely applied to real-world applications. However, its high computational load in the test phase makes it difficult to use in practice. In this paper, we propose hybrid neural network (HNN), a method to accelerate an SVM in the test phase by approximating the SVM. The proposed method approximates the SVM using an artificial neural network (ANN). The resulting regression function of the ANN replaces the decision function or the regression function of the SVM. Since the prediction of the ANN requires significantly less computation than that of the SVM, the proposed method yields faster test speed. The proposed method is evaluated by experiments on real-world benchmark datasets. Experimental results show that the proposed method successfully accelerates SVM in the test phase with little or no prediction loss.     

一种神经网络文本分类器的设计与实现

论文着重介绍了一种基于神经网络的文本分类器,分类器使用神经网络作为分类工具,特征词的词频组成原始特征向量,和神经网络输入层的神经元一一对应。并引入了信息检索中的常用技术——潜在语义索引,训练过程中结合遗传算法,优化神经网络的初始权值。最后对分类器进行了开放性测试,实验表明分类器对文本分类具有较高的平均查全率和平均精度。     

Prediction of wind-induced pressures on a large gymnasium roof using artificial neural networks

The application of artificial neural networks (ANNs) to solve wind engineering problems has received increasing interests in recent years. This paper is concerned with developing two ANN approaches (a backpropagation neural network [BPNN] and a fuzzy neural network [FNN]) for the prediction of mean, root-mean-square (rms) pressure coefficients and time series of wind-induced pressures on a large gymnasium roof. In this study, simultaneous pressure measurements are made on a large gymnasium roof model in a boundary layer wind tunnel and parts of the model test data are used as the training sets for developing two ANN models to recognize the input–output patterns. Comparisons of the prediction results by the two ANN approaches and those from the wind tunnel test are made to examine the performance of the two ANN models, which demonstrates that the two ANN approaches can successfully predict the pressures on the entire surfaces of the large roof on the basis of wind tunnel pressure measurements from a certain number of pressure taps. Moreover, the FNN approach is found to be superior to the BPNN approach. It is shown through this study that the developed ANN approaches can be served as an effective tool for the design and analysis of wind effects on large roof structures.     

基于模糊逻辑和神经网络的电力负荷预测

设计并实现了神经网络和模糊逻辑相结合的综合预测模型进行短期电力负荷预测。由神经网络和模糊逻辑分别对基本负荷和受天气、节假日影响的负荷进行预测,使其在天气突变等情况下也能达到较高的预测精度。采用此模型对石家庄电力系统负荷进行预测分析,取得了令人满意的结果。     

A novel approach for estimating heat transfer coefficients of ethylene glycol–water mixtures

Ethylene glycol–water mixtures (EGWM) are vital for cooling engines in automotive industry. Scarce information is available in the literature for estimating the heat transfer coefficients (HTC) of EGWM using knowledge-based estimation techniques such as adaptive neuro-fuzzy inference systems (ANFIS) and artificial neural networks (ANN) which offer nonlinear input–output mapping. In this paper, the supervised learning methods of ANFIS and ANN are exploited for estimating the experimentally determined HTC. This original research fulfills the preceding modeling efforts on thermal properties of EGWM and HTC applications in the literature. An experimental test setup is designed to compute HTC of mixture over a small circular aluminum heater surface, 9.5 mm in diameter, placed at the bottom 40-mm-wide wall of a rectangular channel 3 mm × 40 mm in cross section. Measurement data are utilized as the train and test data sets of the estimation process. Prediction results have shown that ANFIS provide more accurate and reliable approximations compared to ANN. ANFIS present correlation factor of 98.81 %, whereas ANN estimate 87.83 % accuracy for test samples.     

Enhancing positioning accuracy of GPS/INS system during GPS outages utilizing artificial neural network

Integrated global positioning system and inertial navigation system (GPS/INS) have been extensively employed for navigation purposes. However, low-grade GPS/INS systems generate erroneous navigation solutions in the absence of GPS signals and drift very fast. We propose in this paper a novel method to integrate a low-grade GPS/INS with an artificial neural network (ANN) structure. Our method is based on updating the INS in a Kalman filter structure using ANN during GPS outages. This study focuses on the design, implementation and integration of such an ANN employing an optimum multilayer perceptron (MLP) structure with relevant number of layers/perceptrons and an appropriate learning. As a result, a land test is conducted with the proposed ANN + GPS/INS system and we here provide the system performance with the land trials.     

Automatic classification of Tamil documents using vector space model and artificial neural network

Automatic text classification based on vector space model (VSM), artificial neural networks (ANN), K-nearest neighbor (KNN), Naives Bayes (NB) and support vector machine (SVM) have been applied on English language documents, and gained popularity among text mining and information retrieval (IR) researchers. This paper proposes the application of VSM and ANN for the classification of Tamil language documents. Tamil is morphologically rich Dravidian classical language. The development of internet led to an exponential increase in the amount of electronic documents not only in English but also other regional languages. The automatic classification of Tamil documents has not been explored in detail so far. In this paper, corpus is used to construct and test the VSM and ANN models. Methods of document representation, assigning weights that reflect the importance of each term are discussed. In a traditional word-matching based categorization system, the most popular document representation is VSM. This method needs a high dimensional space to represent the documents. The ANN classifier requires smaller number of features. The experimental results show that ANN model achieves 93.33% which is better than the performance of VSM which yields 90.33% on Tamil document classification.     

A flexible neural network-fuzzy mathematical programming algorithm for improvement of oil price estimation and forecasting

This paper presents a flexible algorithm based on artificial neural network (ANN) and fuzzy regression (FR) to cope with optimum long-term oil price forecasting in noisy, uncertain, and complex environments. The oil supply, crude oil distillation capacity, oil consumption of non-OECD, USA refinery capacity, and surplus capacity are incorporated as the economic indicators. Analysis of variance (ANOVA) and Duncan’s multiple range test (DMRT) are then applied to test the significance of the forecasts obtained from ANN and FR models. It is concluded that the selected ANN models considerably outperform the FR models in terms of mean absolute percentage error (MAPE). Moreover, Spearman correlation test is applied for verification and validation of the results. The proposed flexible ANN–FR algorithm may be easily modified to be applied to other complex, non-linear and uncertain datasets.     

Online implementation of an adaptive calibration technique for displacement measurement using LVDT

The paper presents the design and validation of an online intelligent displacement measurement technique with Linear Variable Differential Transformer (LVDT) using Artificial Neural Network (ANN). The objectives of the proposed work are to design a calibration technique using an optimised neural network model such that it a) produces an output which is linear for the full scale of input range, b) makes the output independent of the variations in supply frequency, the physical parameters of the LVDT, and ambient temperature. The output of an LVDT is converted to a DC signal by using a rectifier circuit. The rectified output is further amplified using a differential amplifier. This voltage signal is acquired onto a computer for further processing using an ANN. The optimisation of the ANN is carried out to find the minimum number of hidden layers along with the number of neurones in each layer to give least Mean Square Error (MSE) and Regression (R) nearing to one. This optimisation is done considering various schemes of ANN, training algorithms, and the transfer function of neurones. Once the ANN model is designed, it is subjected to test with both simulated data and experimental validation. The results confirm the successful achievement of the objectives of this paper and thus avoiding the need for repeated calibration.     

The effect of ICA and PSO on ANN results in approximating elasticity modulus of rock material

Reliable determination/evaluation of the rock deformation can be useful prior any structural design application. Young’s modulus (E) affords great insight into the characteristics of the rock. However, its direct determination in the laboratory is costly and time-consuming. Therefore, rock deformation prediction through indirect techniques is greatly suggested. This paper describes hybrid particle swarm optimization (PSO)–artificial neural network (ANN) and imperialism competitive algorithm (ICA)–ANN to solve shortcomings of ANN itself. In fact, the influence of PSO and ICA on ANN results in predicting E was studied in this research. By investigating the related studies, the most important parameters of PSO and ICA were identified and a series of parametric studies for their determination were conducted. All models were built using three inputs (Schmidt hammer rebound number, point load index and p-wave velocity) and one output which is E. To have a fair comparison and to show the capability of the hybrid models, a pre-developed ANN model was also constructed to estimate E. Evaluation of the obtained results demonstrated that a higher ability of E prediction is received developing a hybrid ICA–ANN model. Coefficient of determination (R²) values of (0.952, 0.943 and 0.753) and (0.955, 0.949 and 0.712) were obtained for training and testing of ICA–ANN, PSO–ANN and ANN models, respectively. In addition, VAF values near to 100 (95.182 and 95.143 for train and test) were achieved for a developed ICA–ANN hybrid model. The results indicated that the proposed ICA–ANN model can be implemented better in improving performance capacity of ANN model compared to another implemented hybrid model.