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We consider the system-level self-diagnosis of multiprocessor and multicomputer systems under the generalized comparison model (GCM). In this diagnosis model, a set of tasks is assigned to pairs of nodes and their outcomes are compared by neighboring nodes. The collections of all comparison outcomes, agreements and disagreements among the nodes, are used to identify the set of faulty nodes. We consider...
DDoS attack is a major Internet security problem-DoS is that lots of clients simultaneously send service requests to certain server on the internet such that this server is too busy to provide normal services for others. Attackers using legitimate packets and often changing package information, so that traditional detection methods based on feature descriptions is difficult to detect it. This paper...
Nonparametric Wilcoxon regressors, which generalize the rank-based Wilcoxon approach for linear parametric regression problems to nonparametric neural networks, were recently developed aiming at improving robustness against outliers in nonlinear regression problems. It is natural to investigate if the Wilcoxon approach can also be generalized to nonparametric classification problems. Motivated by...
Customer value is the value that based on customer's perceived value, which is a comprehensive comparison between perceived benefit and perceived cost. There are closely relationship of customer value and enterprise performance. The improvement of customer value contribute to enterprise performance. Based on the above theoretical analysis, the customer value was divided into 3 types that is "good",...
This research is on presenting a new approach for cardiac arrhythmia disease classification. The proposed method uses Modular neural network (MNN) model to classify arrhythmia into normal and abnormal classes. We have performed experiments on UCI Arrhythmia data set. Missing attribute values of this data set are replaced by closest column value of the concern class. We have constructed neural network...
Based on the annual report data between 1995 and 2005 of all listed companies (LCs), the 25 initial financial indexes, widely used by experts and researchers aboard and at home, was deduced to 14 effective evaluation indicators using factor analysis and principal component analysis (PCA). The 14 evaluation indicators, covering five aspects for comprehensive evaluation of competitiveness of LC, retain...
In this research, the combination of modal data is used to identify the damage of a FEM model using neural networks. The identification ability with different levels of noise and incomplete mode shapes are also investigated. It has been proved that the neural network using combination of modal parameters as input has a excellent identification ability with ideal error tolerance and robustness. Numberical...
The location and size of internal wood defects are nondestructively determined using experimental modal analysis and artificial neural network in this study. The different defect sizes and locations were simulated by removing mass from intact wood specimens. At room temperature in the laboratory, free vibration testing was conducted to generate the frequency response functions (FRF) of intact and...
Pattern recognition is very challenging multidisciplinary research area attracting researchers and practitioners. Gesture recognition is a specialized pattern recognition task with the goal of interpreting human gestures via mathematical models. One of the usages of gesture recognition is the sign language recognition which is the basic communication method between deaf people. Since there is lack...
In this paper, a new kind of three-stage neural network was developed to identify the sorts of the biological surface. The visible spectrum (from 380nm to 780nm) of the micro areas with some specks on the surface of the apples was measured with the self-made fiber sensor spectrometer. To sort the apples, A kind of BP-ANN with single hidden layer was devised to identify the characteristics on the biological...
In order to simulate a nonlinear system, A BP neural network can be used. First the question is analyzed, we can know what we use to input to the system, the dimensions of the input vectors will be the number of the input layer neurons, The number of the output layer neurons depends on the output parameters, The numbers of the hidden layer neurons depends both on the input layer number and the output...
Background: The majority of software faults are present in small number of modules, therefore accurate prediction of fault-prone modules helps improve software quality by focusing testing efforts on a subset of modules. Aims: This paper evaluates the use of the faults-slip-through (FST) metric as a potential predictor of fault-prone modules. Rather than predicting the fault-prone modules for the complete...
An essential element of electric utility resource planning is the long term forecast of the electricity consumption. This paper presents an approach to forecast annual electricity consumption by using artificial neural network based on historical data for Malaysia. It involves developing several ANN designs and selecting the best network that can produce the best results in terms of its accuracy....
Two damage anomalous filters which were set up by BP neural network have been used to alarm the damage in structural members. After dealing with eigenparameter extracted from damaged and intact structure, different input data is considered for setting up different damage warning anomalous filters. Filter □: the first eight natural frequencies are chosen as input data of network. Filter □: one mode...
The influence of blasting vibration to surroundings around the blasting area can not be ignored, in order to guarantee the safety of surroundings around blasting area, blasting vibration forecasting model based on neural network is established by improved BP neural network in this paper. The inputs of the model are the largest single fire dynamite, height difference and horizontal distance between...
Two popular hazards in supervised learning of neural networks are local minima and over fitting. Application of the momentum technique dealing with the local optima has proved efficient but it is vulnerable to over fitting. In contrast, deployment of the early stopping technique might overcome the over fitting phenomena but it sometimes terminates into the local minima. This paper proposes a hybrid...
This paper describes the reliability and validation of prediction models of LAN/WLAN integration network. An improved PSO algorithm is used to optimize the weight of BP neural network. Support vector machine (SVM) is used in network reliability prediction. The LAN/WLAN integration network reliability prediction models are established with three methods (BP neural network, improved BP neural network...
The theories of using neural network to construct the fault dictionary of analog circuits were studied The response signatures of fault and fault-free circuit under test were generated during a simulation of the circuit before the diagnosis phase and are used to train an neural network. If we have obtained the response signature of the circuit under test on line, we can identify the fault by inputting...
In this paper, a model of orthogonal multiwavelets neural network ensemble is proposed. The neural network ensemble consists of component orthogonal multiwavelets neural networks where each component neural network is trained by back propagation (BP) algorithm and with orthogonal multiwavelets functions in the hidden layer. Due to the orthogonality of orthogonal multiwavelets functions, all the hidden...
In this paper, a type of improved hybrid strategy is used to carry out the image segmentation of gold immuno chromato graphic test strip, which combines the reactive tabu search (RTS) algorithm and error back propagation (EBP) neural network. This hybrid strategy can solve the problems which exist in image segmentation of test strip: the area of test strip is pretty small; the breadth of test line...
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