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Cataclysmic variable stars (CVs) are used to study evolutionary theories especially models for close binary stars. The further research of CVs is constrained by the number of samples especially spectroscopic samples since CVs is a kind of rare and special celestial objects. LAMOST-DR2 is the second data release of Guoshoujing telescope, which provides an unprecedented opportunity to search for CVs...
One of the most popular machine learning algorithms, ANN (Artificial Neural Network) has been extensively used for Data Mining, which extracts hidden patterns and valuable information from large databases. Data mining has extensive and significant applications in a large variety of areas. This paper introduces a new adaptive Higher Order Neural Network (HONN) model and applies it in data mining tasks...
On the basis of studying the working principle of discrete fuzzy control, according to the structural characteristic and the ability of RBF neural network approaching to any unknown function, the clustering and nonlinear mapping function of RBF neural network are used to implement discrete fuzzy inference and control. If only a RBF neural network is suitably designed and well trained by a discrete...
Using grey system, satisfaction mining (DM) technology and radial basis function (RBF) neural network method, the combined model of grey system and RBF neural network is setup, which aims at solving the problems of E-government. The results show that, in short-term prediction, grey system is an effective way and RBF has perfect ability to study. The combined grey neural network (CGNN) has the dual...
Using the theory of grey system, data mining technology and radial basis function (RBF) neural network method, a new model, the combined model of grey system and RBF neural network, is setup, which aims at solving the user's received data safety analysis. The results show that, in short-term prediction of data safety, GM is an effective way and RBF has perfect ability to study and map. The combined...
Automobile sells system plays an important role in automobile sales area, through the whole produce and management. Some forecast models have had unilateralism in some side nowadays, such as ARMA model. For example, the data of non-linearity has some error by ARMA model. This paper, assembles curve -regression model, Time Series Decomposition Model and RBF neural networks according to the weight distribution...
During the operation of the industrial process, one of the optimal control objectives is to control some technique indices that represent the quality, efficiency and consumption of the product processing into their targeted ranges. So, it is important that technique indices can be obtained accurately and opportunely. However, in some industrial processes, technique indices can not be measured on-line...
The original electrical signals in Crassula portulacea were tested by a touching test used platinum sensors in a system of self-made double shields. Tested data of the electrical signals were denoised by the wavelet soft threshold and using Gaussian radial base function (RBF) as the time series at a delayed input window chosen at 50. An intelligent RBF forecasting system was set up to forecast the...
The two-level synthetical evaluating model is presented in this paper based on the factors of substance and people which affect the industrial product model design and the fuzzy feature existed in the mobile phone model evaluation. And a synthetical evaluation system is formed as to mobile phone by the way of the radial basis function neural network. And it gained a good result from evaluating. Experimental...
BP neural network has the shortcoming of over-fitting, local optimal solution, which affects the practicability of BP neural network. RBF neural network is a feedforward neural network, which has the global optimal closing ability. However, the parameters in RBF neural network need determination. Particle swarm optimization is presented to choose the parameters of RBF neural network. The particle...
Accurate traffic flow forecasting is significant to the intelligent traffic guidance and traffic control. RBF neural network (RBFNN) is a feed-forward neural network with one hidden layer and can uniformly approximate any continuous function to a prospected accuracy. Compared with the back propagation feed forward network (BPNN), the RBFNN requires less computation time for learning and higher forecasting...
Random early detection (RED) is a network congestion control algorithm which calculates the packet-loss ratio according to current length of average queue. This paper describes an improved RED algorithm: Firstly, predicts network flows with RBF neural network in order to forecast queue length much earlier, and then, fits the packet-loss-ratio of RED algorithm according to some special points using...
In this paper, we address the tracking problem for a class of switched nonlinear singular SISO/MIMO systems based on RBF neural network. Adaptive neural network switched controller is designed for the above problem under the case that singular matrix of all subsystems is same. The RBF neural network is used to approximate the unknown part of switched nonlinear singular systems, and the approximation...
Aiming at the characteristics of mark-up decision, 18 main factors which influenced the mark-up rate were defined based on previous experiences and research results to build index system of the mark-up rate decision model. Then RBF neural network model was used for markup rate decision in bidding and pricing with MATLAB neural network toolbox.
Power transmission line insulator is an important part for power system security. Because insulator has complex operating environment and its infection factors interact on each other, the diagnosis of insulator running state is very difficult. It is needed to use some useful information to conclude insulator operating state. Here, RBF neural network is employed to identify and predict the needed time...
This paper presents a hybrid short-term traffic flow forecast technology. For the uncertainty, the short-term traffic flow forecast is complicated, and the accuracy is not high. This strategy combines the RBF neural network and ant colony clustering algorithm to forecast the traffic flow. It used ant colony clustering algorithm to get the centers of hidden layer neurons. To find the best clustering...
In order to solve the problem of big customer churn for fixed communication network operators, a prediction model of customer churn in fixed communication network is first established based on RBF neural network, and it can make prediction on customer churn. Then subdivides customers by Analog Complexion Cluster to guide and help manage marketing and related work.
Depth parking control is important for the HTPE (HydroThermal Plume Explorer). It aims to have a more accurate parking depth and a less oscillation at the target depth. The plant approaching and motion forecasting model based on RBF neural network is built to self-adjust and approach online motion law of HTPE in ideal circumstance of the sea, and simulate output of given control quantity after an...
In order to improve diagnosis precision and decreasing misinformation diagnosis, according to the intelligence complementary strategy, a new complex intelligent fault diagnosis method based on rough sets theory and RBF neural network is presented. Firstly, basis on data pretreatment, the fault diagnosis decision table is formed, and continuous datum are discretized by using hybrid clustering method...
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