Web proxy cache replacement scheme based on back-propagation neural network

Web proxy caches are used to reduce the strain of contemporary web traffic on web servers and network bandwidth providers. In this research, a novel approach to web proxy cache replacement which utilizes neural networks for replacement decisions is developed and analyzed. Neural networks are trained to classify cacheable objects from real world data sets using information known to be important in web proxy caching, such as frequency and recency. Correct classification ratios between 0.85 and 0.88 are obtained both for data used for training and data not used for training. Our approach is compared with Least Recently Used (LRU), Least Frequently Used (LFU) and the optimal case which always rates an object with the number of future requests. Performance is evaluated in simulation for various neural network structures and cache conditions. The final neural networks achieve hit rates that are 86.60% of the optimal in the worst case and 100% of the optimal in the best case. Byte-hit rates are 93.36% of the optimal in the worst case and 99.92% of the optimal in the best case. We examine the input-to-output mappings of individual neural networks and analyze the resulting caching strategy with respect to specific cache conditions.     

基于角微特征的神经网络识别人脸特征

提出一种利用人脸角微特征几何特性的图像预处理,建立BP神经网络识别人脸特征模型的方法。研究了角微特征提取和具体算法,讨论了BP网络结构的设计,输入、输出层设计和隐藏层节点选取问题。微特征提取,可以降低网络输入维度,对于识别不同角度、不同表情的人脸图像提供了可能性。利用ORL人脸图像数据库做实验,结果表明此方法有效。     

Automatic inspection system of LED chip using two-stages back-propagation neural network

This study proposed an automatic LED defect detection system to investigate the defects of LED chips. Such defects include fragment chips, scratch marks and remained gold on the pad area, scratch marks on the luminous zone, and missing luminous zone respectively. The system was based on positioning and image acquisition, appearance feature recognition, and defect classification. The normalized correlation coefficient method was used to locate the chip and acquire its image, the K-means clustering method was used to distinguish the appearance, pad area, and luminous zone of chips. In terms of pad area detection, histogram equalization was used to enhance the pad image contrast, and statistical threshold selection and morphological closing were applied to modify the impure points in the pad. Feature values of the pad area were then calculated. The optimal statistical threshold separated the luminous zone and background from the substrate. After processed with closing operation, features of the luminous zone were extracted. Finally, features of each part were clarified by an efficient two-step back-propagation neural network, where a designed appearance classifier and an internal structure classifier were used for recognition. From experiments, total recognition rate of this study achieved 97.83 %, proving that the detection method proposed by this study can efficiently detect LED chip defects.     

基于PSO和BP复合算法的模糊神经网络控制器

为了克服单独应用粒子群算法（PSO）或BP算法训练模糊神经网络控制器参数时存在的缺陷,提出了一种训练模糊神经网络参数的PSO＋BP算法。该算法将二者相结合,即在PSO算法中加入一个BP算子,以充分利用PSO算法的全局寻优能力和BP算法的局部搜索能力,从而更有效地提高其收敛速度、训练效率和提高该模糊神经网络控制器的控制效果。最后的仿真实验结果验证了该基于PSO＋BP复合算法的模糊神经网络控制器的有效性和可行性。     

A three-layer back-propagation neural network for spam detection using artificial immune concentration

In this paper, a three-layer back-propagation neural network (BPNN) is employed for spam detection by using a concentration based feature construction (CFC) approach. In the CFC approach, ‘self’ and ‘non-self’ concentrations are constructed through ‘self’ and ‘non-self’ gene libraries, respectively, to form a two-element concentration vector for expressing the e-mail efficiently. A three-layer BPNN with two-element input is then employed to classify e-mails automatically. Comprehensive experiments are conducted on two public benchmark corpora PU1 and Ling to demonstrate that the proposed CFC approach based BPNN classifier not only has a very much fast speed but also achieves 97 and 99% of classification accuracy on corpora PU1 and Ling by just using a two-element concentration feature vector.     

基于BP神经网络的专利自动分类方法

提出了一种基于后向传播神经网络的专利自动分类方法.通过中文分词从专利文件集中提取特征项,并根据特征项在专利文件中出现的频率赋予其权重,从而将每篇专利文件表示为一个特征项向量.为取得较好的BP神经网络(BPN)训练效果,使用X2统计方法进行特征向量降维,并使用BPN专利分类器进行专利文件分类.用国际分类号为H02下的专利文件作为测试数据,取得了较好的分类效果.     

基于多二维RBF神经网络的航空雷达信号分选

针对航空雷达信号分选中侦察装备普遍存在的信号分选实时性差,分选结果经常出现增批、漏批现象的缺点.为了提高侦察系统在复杂电磁环境下准确快速的分选出雷达辐射源信号,根据径向基(RBF)神经网络通过理想数据训练后能够对未知数据进行分类的特点,将径向基神经网络算法用于对航空雷达侦察信号的分选,在此基础上提出了一种新型多二维径向基神经网络结构,通过与BP网络、RBF网络的对比,多二维径向基神经网络的识剐率优于其它几种网络,而且其结构便于实现.通过试验结果可以得出,多二维径向基神经网络能够提高雷迭信号分选的准确率.     

A multi-industry bankruptcy prediction model using back-propagation neural network and multivariate discriminant analysis

The accurate prediction of corporate bankruptcy for the firms in different industries is of a great concern to investors and creditors, as the reduction of creditors’ risk and a considerable amount of saving for an industry economy can be possible. This paper presents a multi-industry investigation of the bankruptcy of Korean companies using back-propagation neural network (BNN). The industries include construction, retail, and manufacturing. The study intends to suggest the industry specific model to predict bankruptcy by selecting appropriate independent variables. The prediction accuracy of BNN is compared to that of multivariate discriminant analysis.The results indicate that prediction using industry sample outperforms the prediction using the entire sample which is not classified according to industry by 6–12%. The prediction accuracy of bankruptcy using BNN is greater than that of MDA. The study suggests insights for the practical industry model for bankruptcy prediction.     

Integration of fuzzy neural network and artificial immune system-based back-propagation neural network for sales forecasting using qualitative and quantitative data

Sales forecasting plays a very important role in business operation. Many researches generally employ statistical methods, such as regression or auto-regressive integrated moving average model, to forecast the product sales. However, they only can consider the quantitative data. Some exogenous qualitative variables have more influence on forecasting result. Thus, this study attempts to propose a integrated forecasting system which is able to consider both quantitative and qualitative factors to achieve a more comprehensive result. Basically, fuzzy neural network is first employed to capture the expert knowledge regarding some qualitative factors. Then, it is combined with the time series data using an artificial immune system based back-propagation neural network. A laptop sales data set provided by a distributor in Taiwan is applied to verify the proposed approach. The computational result indicates that the proposed approach is superior to other forecasting methods. It can be used to decrease the inventory costs and enhance the customer satisfaction.     

A modified Smith predictive scheme based back-propagation neural network approach for FOPDT processes control

In this paper, a novel control scheme to deal with process uncertainties in the form of disturbance loads and modelling errors, as well as time-varying process parameters is proposed by applying the back-propagation neural network (BPNN) approach. A BPNN predictive controller that replaces the entire Smith predictor structure is initially trained offline. Lyapunov direct method is used to prove that the convergence of this BPNN is guaranteed by selecting a suitable learning rate during the learning process. However, the Smith predictor based BPNN control is an off-line training based algorithm, which is a time consuming method and requires a known process plant input from the controller. A desired control input to the process is difficult to obtain for the training of the network. As a result a group of proper training data (target control inputs and outputs) can hardly be provided. In order to overcome this problem, a BPNN with an on-line training algorithm is introduced for the control of a First Order plus Dead Time (FOPDT) process. The stability analysis is carried out using the Lyapunov criterion to demonstrate the network convergence ability. Simulation results show that this proposed online trained neural Smith predictor based controller provides excellent robustness to process modelling errors and disturbance loads, and high adaptability to time varying processes parameters.     

基于BP神经网络二维码区域提取

在复杂背景下的二维码区域定位一直是QR Code二维条码解码过程中的难题之一。二维码区域扫描定位是通过二维码的图形特点来实现,其存在扫描定位效率较低的缺点。为此提出在扫描定位之前通过图像处理结合BP神经网络实现QR Code二维码条码区域提取方法。火车票通过图像预处理得到可能是二维码的区域块,提取经图像处理后的二维码区域块图像特征并结合BP神经网络过滤出正确的二维码区域。此方法实现了寻找一幅图像中二维码区域的图像,结合二维码图形扫描定位方法,提高了二维码扫描定位的效率,得到了较好的效果。     

A user authentication system using back-propagation network

Information security has been a critical issue in the field of information systems. One of the key factors in the security of a computer system is how to identify the authorization of users. Password-based user authentication is widely used to authenticate a legitimate user in the current system. In conventional password-based user authentication schemes, a system has to maintain a password table or verification table which stores the information of users IDs and passwords. Although the one-way hash functions and encryption algorithms are applied to prevent the passwords from being disclosed, the password table or verification table is still vulnerable. In order to solve this problem, in this paper, we apply the technique of back-propagation network instead of the functions of the password table and verification table. Our proposed scheme is useful in solving the security problems that occurred in systems using the password table and verification table. Furthermore, our scheme also allows each user to select a username and password of his/her choice.     

Multiple costs based decision making with back-propagation neural networks

The current research investigates a single cost for cost-sensitive neural networks (CNN) for decision making. This may not be feasible for real cost-sensitive decisions which involve multiple costs. We propose to modify the existing model, the traditional back-propagation neural networks (TNN), by extending the back-propagation error equation for multiple cost decisions. In this multiple-cost extension, all costs are normalized to be in the same interval (i.e. between 0 and 1) as the error estimation generated in the TNN. A comparative analysis of accuracy dependent on three outcomes for constant costs was performed: (1) TNN and CNN with one constant cost (CNN-1C), (2) TNN and CNN with two constant costs (CNN-2C), and (3) CNN-1C and CNN-2C. A similar analysis for accuracy was also made for non-constant costs; (1) TNN and CNN with one non-constant cost (CNN-1NC), (2) TNN and CNN with two non-constant costs (CNN-2NC), and (3) CNN-1NC and CNN-2NC. Furthermore, we compared the misclassification cost for CNNs for both constant and non-constant costs (CNN-1C vs. CNN-2C and CNN-1NC vs. CNN-2NC). Our findings demonstrate that there is a competitive behavior between the accuracy and misclassification cost in the proposed CNN model. To obtain a higher accuracy and lower misclassification cost, our results suggest merging all constant cost matrices into one constant cost matrix for decision making. For multiple non-constant cost matrices, our results suggest maintaining separate matrices to enhance the accuracy and reduce the misclassification cost.     

基于独立分量分析和BP网络的电子鼻模式识别

为了提高电子鼻对混合气体的识别率,针对气体传感器阵列的交叉敏感特性,探讨了在电子鼻系统中基于独立分量分析(ICA)算法与BP神经网络相结合进行模式识别的可行性。并对4个气体传感器组成的电子鼻对4种气体混合物所测得的原始数据进行处理,结果表明:ICA算法对数据进行有效预分类,减少了样本之间的相关性,将生成的新样本作为BP网络的输入,使网络结构简化,在保证一定正确率的前提下,大大提高网络的学习速度。利用该方法可以提高电子鼻识别混合气体的准确率。     

基于细胞神经网络的灰度图像边缘提取

针对细胞神经网络(cellular neural network,CNN),研究了图像边缘提取的过程,给出算法流程,阐述了算法实现过程中的几个关键步骤.对二值图像和灰度图像,分别采用基于CNN的算法和传统算子(prewitt、sobel、canny)进行边缘提取,定性分析比较了两类算法在性能上的优劣.实验结果表明,基于CNN的算法在硬件实现上能够高速并行计算,而且处理速度与图像大小无关,能够实现图像实时处理.     

Multi-layer compression algorithm for 3D deformed mesh based on multi library wavelet neural network architecture

Multimedia Tools and Applications - We propose in this paper a 3D mesh compression algorithm for 3D deformation objects to facilitate the transmission of deformed object to another. This algorithm...     

Variational PDE based image restoration using neural network

Two variational partial differential equations as regularisation terms are proposed for the image restoration model based on the modified Hopfield neural network. One is based on a harmonic model and the other is based on a total variation model. The performance of these regularisation terms is analysed from the viewpoint of nonlinear diffusion. It can be shown that the two proposed restoration models have edge-preserving performance superior to that of the traditional restoration model. Two algorithms have been proposed on the basis of the harmonic restoration model and the total variation model. Experimental results show that the proposed algorithms are more effective than the traditional algorithm     

Recurrent Sigma-Pi-linked back-propagation network

A recurrent Sigma-Pi-linked back-propagation neural network is presented. The increase of input information is achieved by the introduction of “higher-order≓ terms, that are generated through functional-linked input nodes. Based on the Sigma-Pi-linked model, this network is capable of approximating more complex function at a much faster convergence rate. This recurrent network is intensively tested by applying to different types of linear and nonlinear time-series. Comparing to the conventional feedforward BP network, the training convergence rate is substantially faster. Results indicate that the functional approximation property of this recurrent network is remarkable for time-series applications.