Network securitymanagement based on data fusion technology

This paper uses a radial basis function (RBF) neural network for star identification. A feature vector consisting of distance and angle cosine values is constructed as a library of recognition feature stars and input to the neural network. Then, the network’s output is the number of the corresponding primary star. Comparing the training efficiency and robustness of the grid algorithm with the BP neural network and radial basis function neural network for star identification, the experimental results show that the radial basis function neural network has a strong anti-interference ability against noise and pseudo-star interference. The following conclusions are drawn from the simulation tests: the sample recognition rate of radial basis function neural network algorithm can reach 96% for star position and star addition noise; when the proportion of pseudo-star reaches 20%, the sample recognition rate of radial basis function neural network algorithm is still higher than grid algorithm and BP neural network, which indicates that the radial basis function neural network algorithm has the solid anti-interference ability to both noise and pseudo-star. Therefore, the radial basis function neural network algorithm based on star Identification has specific application prospects.

A new method based on the integration of principal component analysis (PCA) and radial basic function (RBF) neural network is put forward for selecting the real estate project. Firstly, principal component analysis (PCA) is used to reduce the evaluation index dimensions. And then, radial basic function (RBF) neural network is used to evaluate the real estate projects. In order to grasp this method better, finally, the paper provides a case to demonstrate the application of this method in selecting the real estate project. The case has shown that the method applied to select the real estate project is feasible and reliable.

The study compared the forecasting performance of two multiple-input and multiple-output (MIMO) and two single-input and single-output (SISO) neural networks using 4G network traffic data aggregated into daily, weekly and monthly time spans. To explore the best configuration of SISO and MIMO neural networks, the empirical traffic data of 1-input, 2- input and 3-input were used together with varying the parameters of the models. The study concluded that for 2-input, MIMO Radial basis function neural (RBFN) network performed better than the 2-input MIMO Multilayer perceptron (MLP) neural network in predicting the traffic data. In the case of 3-input, MLP network was found to be more efficient than RBFN network. In the scenario of SISO architecture, the MLP network outperformed the RBFN network for 4G daily, weekly and monthly traffic data.

A wind power system (WPS) involving a flywheel energy storage (FES) unit is proposed to reduce the fluctuation of output power due to the intermittent nature of wind power. The control of the FES unit is based on a wind speed forecasting technique by the RBF neural network. A new control strategy adopting different reference DC bus voltages under different forecasting wind speeds is presented to improve the output power regulation capability of the FES unit. The wind speeds in the next time array can be accurately forecasted by the radical basis function (RBF) neural network. The mathematical model of a stand-alone wind power generation system including a FES unit is established and simulated by Matlab/Simulink. The simulation results verify the correctness of the system model and the effectiveness of the control method.

Dissolved oxygen (DO) as one of the most fundamental parameters of water quality plays a vital role in aquatic life. This study was conducted to predict DO, biological oxygen demand (BOD), and chemical oxygen demand (COD) in an intensive rainbow trout rearing system with different biomass (B). The multilayer perceptron (MLP) and the radial basis function (RBF) neural networks were employed for evaluating the impacts of food parameters (crude protein (CP), consumed feed (CF)), fish parameters (different values of B, and weight gain (WG)), and water quality parameters including temperature (T) and flow rate (Q) on variation of DO, BOD, and COD concentrations. This study's results showed that although both MLP and RBF neural networks are capable to estimate DO, BOD, and COD concentrations, RBF neural network showed better performance compared to MLP neural network. The results of sensitivity analysis indicated that the parameter CF has the highest effect on DO concentration estimation. Independent variables CF, CP, WG, and B showed the highest to the lowest rank of impacts on BOD estimation, respectively. The results also illustrated a decreasing trend of the effects on the estimation error of COD changes simulation by all independent variables, including B, T, WG, CF, CP, and Q, respectively. RBF neural network based on better stability and generalization ability with average root mean square error (RMSE) and mean absolute percentage error (MAPE) values of less than 0.12 and 3% was superior to MLP in DO, BOD, and COD concentration prediction. Moreover, CF was identified as the most effective factor in estima12tion process. Based on the present study results, there are direct relationships between DO, BOD, and COD concentrations and water quality parameters, fish parameters, and food parameters. Food parameters relative to fish and water quality parameters imposed the greatest effects. Improvement in feeding process such as application of intelligence feeding methods and change in fish diet and feeding time can considerably reduce losses in production system. Graphical abstract.

A evaluation model based on the integration of analytic hierarchy process (AHP)-rough set theory (RS) and radial basic function (RBF) neural network is put forward for grasping the hydropower project financing risk. Firstly, the evaluation indicator system is constructed by AHP, then the evaluation indicators are discretized by RS neural network. And then, RBF neural network is used to evaluate the hydropower project financing risk. In order to grasp this evaluation model better, finally, the paper provides an example to demonstrate the application of this evaluation model.

Digital image recognition of plant diseases could reduce the dependence of agricultural production on the professional and technical personnel in plant protection field and is conducive to the development of plant protection informatization. In order to find out a method to realize image recognition of plant diseases, four kinds of neural networks including backpropagation (BP) networks, radial basis function (RBF) neural networks, generalized regression networks (GRNNs) and probabilistic neural networks (PNNs) were used to distinguish wheat stripe rust from wheat leaf rust and to distinguish grape downy mildew from grape powdery mildew based on color features, shape features and texture features extracted from the disease images. The results showed that identification and diagnosis of the plant diseases could be effectively achieved using BP networks, RBF neural networks, GRNNs and PNNs based on image processing. For the two kinds of wheat diseases, the best prediction accuracy was 100% with the fitting accuracy equal to 100% while BP networks, GRNNs or PNNs were used, and the best prediction accuracy was 97.50% with the fitting accuracy equal to 100% while RBF neural networks were used. For the two kinds of grape diseases, the best prediction accuracy was 100% with the fitting accuracy equal to 100% while BP networks, GRNNs or PNNs were used, and the best prediction accuracy was 94.29% with the fitting accuracy equal to 100% while RBF neural networks were used.

Model reference adaptive sliding mode control (MRASMC) using radical basis function (RBF) neural network (NN) is proposed to control the single-phase active power filter (APF). The RBF NN is utilized to approximate nonlinear function and eliminate the modeling error. AC side model reference adaptive current controller not only guarantees the globally stability of the APF system but also generate the compensating current to track the harmonic current accurately. Moreover, a sliding mode controller based on exponential approach is designed to improve the tracking performance of DC side voltage. Simulation results demonstrate that MRASMC using RBF NN can improve the adaptability and robustness of the APF system and track the given instructional signal quickly.

Radial basis function (RBF) neural network possesses the characteristics of fast training speed, reaching the global minimum and escaping from local minimum, et al. Under fewer samples, the RBF neural network model (RBFNN-based model) for credit comprehensive evaluation is established according to actual enterprise's credit state. For the 6 non-training enterprises, the comparison of the calculated results of several models shows that the accuracy of the RBFNN-based model established in this paper is the highest. It is effective and suitable to apply RBFNN to enterprise credit comprehensive evaluation

Accurate diagnosis of cancers is of great importance for doctors to choose a proper treatment. Furthermore, it also plays a key role in the searching for the pathology of cancers and drug discovery. Recently, this problem attracts great attention in the context of microarray technology. Here, we apply radial basis function (RBF) neural networks to this pattern recognition problem. Our experimental results in some well-known microarray data sets indicate that our method can obtain very high accuracy with a small number of genes.

In the process of moisture content's measurement with microwave resonant cavity based on RBF (radial basis function) neural network, the relationship among resonant frequency, quality factor and environmental temperature (the inputs of network) and moisture content (the output of network) is multiple non-linear. The performance of multiple non-linear regression algorithm is the major factor that determines the accuracy of measurement. A regression algorithm based on a RBF neural network is put forward to improve the measurement result in this paper. The RBF neural network regression algorithm can effectively avoid the BP algorithm disadvantages such as getting into local minimum and the low efficiency, and has the advantages such as high generalization precision and rapid convergence. Consequently, the measurement accuracy is enhanced. In this paper, experimental results show that the high-precision measurement value of moisture content can be obtained with the multiple non-linear regression algorithm based on RBF neural network.

RBF (Radial Basis Function) neural network is used to predict the magnitude of earthquake in this article. The self-adaptive and nonlinear approach abilities of RBF neural network are suitable to process the complexity of the production mechanism of earthquake. Firstly, RBF neural network is used to learn the data which include the information of earthquake. Then, use the trained RBF neural network to predict the test samples. The reasonable results indicate that the method has certain application value.

A fault detection and diagnosis method for the hydraulic servo system based on adaptive threshold and self-organizing map (SOM) neural network is proposed in this study. The nonlinear, time-varying, fluid-solid coupling properties of the hydraulic servo system are considered. Fault detection is realized based on a two-stage radial basis function (RBF) neural network model. The first-stage RBF neural network is adopted as a fault observer for the hydraulic servo system; the residual error signal is generated by comparing the estimated observer output with the actual measurements. To overcome the drawback of false alarms when the traditional fixed fault threshold is used, an adaptive threshold producer is established by the second-stage RBF neural network. Fault occurrence is detected by comparing the residual error signal with the adaptive threshold. When a system fault is detected, the SOM neural network is employed to implement fault classification and isolation by analyzing the features of the residual error signal. Three types of common faults are simulated to verify the performance and effectiveness of the proposed method. Experimental results demonstrate that the proposed method based on adaptive threshold and SOM neural network is effective in detecting and isolating the failure of the hydraulic servo system.

Due to the rapid development of precision manufacturing technology, much research has been conducted in the field of multisensor measurement and data fusion technology with a goal of enhancing monitoring capabilities in terms of measurement accuracy and information richness, thereby improving the efficiency and precision of manufacturing. In a multisensor system, each sensor independently measures certain parameters. Then, the system uses a relevant signal-processing algorithm to combine all of the independent measurements into a comprehensive set of measurement results. The purpose of this paper is to describe multisensor measurement and data fusion technology and its applications in precision monitoring systems. The architecture of multisensor measurement systems is reviewed, and some implementations in manufacturing systems are presented. In addition to the multisensor measurement system, related data fusion methods and algorithms are summarized. Further perspectives on multisensor monitoring and data fusion technology are included at the end of this paper.

Artificial Neural Network (ANN) simulates the structure and function of human brain. It has the abilities of parallel information processing, distributed storage and self-learning and reasoning. ANN features fault tolerance, nonlinearity, nonlocality, nonconvexity, etc., and is suitable for identifying and mapping fuzzy information or complex nonlinear relationship. Combined with the characteristics of domestic water consumption, industry water consumption and agriculture water consumption, the influencing factors are analyzed. A Radial Basis Function (RBF) Neural Network model is established for water demand prediction, using 17 water demand predication factors as input of the network. On output layer, the four nodes include urban household water demand, rural household water demand, industrial water demand and agricultural water demand. Dynamic Clustering Learning algorithm is used to determine RBF width, cluster center, number of nodes in hidden layer and weight. The number of hidden layer determined by network learning is 8. The relative error of three years are 2.74%, 3.33% and 1.41% respectively. The results show that RBF neural network has such advantages that the output is independent the initial weight value and the convergence speed is faster. And a better forecasting result is achieved through such a model.