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The photoelectric conversion efficiency of photovoltaic cells is mainly affected by two factors, two factors are the operating temperature of the photovoltaic cell and the irradiance of the sun. In order to improve the photoelectric conversion efficiency of photovoltaic cells, combining with the two factors that affect photoelectric conversion efficiency of photovoltaic cells and the merits and demerits...
With the emergence of network virtualization, the infrastructure can be effectively integrated to overcome the "ossification" of the Internet. The biggest challenge in network virtualization is the problem of virtual network embedding. Unfortunately, most of the researches on virtual network embedding only focus on static algorithms, which allocate fixed or invariable resources to virtual...
Metamorphic testing has been successfully used in many different fields to solve the test oracle problem. However, how to find a set of appropriate metamorphic relations for metamorphic testing remains a complicated and tedious task. Recently some machine learning approaches have been proposed to predict metamorphic relations. These approaches predicting single label metamorphic relation can alleviate...
This paper proposes a prediction model for forecasting the hard landing problem. The landing phase has been demonstrated the most dangerous phase in flight cycle for fatal accidents. The landing safety problem has become one of the hot research problems in engineering management field. The study concentrates more on the prediction and advanced warning of hard landing. Firstly, flight data is preprocessed...
The test of bearing capacity of bolt is very important for bolt quality test. Improper parameter of radial basis function (RBF) neural network may lead to large network convergence error and worse generalization capacity. Particle swarm optimization (PSO) is used to optimize the parameters in the study of improved RBF neural network. First, eigenvector respecting the bearing capacity of bolt is chosen...
In order to increase the precision of traffic flow prediction, a short-time prediction method based on RBF neural network is put forward and an improved artificial bee colony (ABC) algorithm is used to train the RBF neural network. The improved artificial bee colony algorithm is described as follows: firstly, the order of employed bees is arranged according to the fitness value in artificial bee colony...
The motion trajectory of nodes in indoor environment is relatively fixed because of the spatial constraint. In addition, mobile node usually moves according to some rules of its own. The localization error would increase when mobile nodes in indoor wireless sensor networks cannot receive the location information sent from anchor nodes due to some unknown transient disturbance. To minimize the localization...
Spectrum prediction is a key technology of cognitive radio, which can help unlicensed users to determine whether the licensed user's spectrum is idle. Based on radial-basis function (RBF) neural network, this paper proposed a spectrum prediction algorithm with K-means clustering algorithm (K-RBF). This algorithm could predict the spectrum holes according to the historical information of the licensed...
Traditional preferred path mining algorithms cannot accurately identify the level of user interest in a web page, and they are rather complex. In order to solve these problems, the paper proposed an improved calculation method which evaluated the level of user interest in a web page and a mining algorithm of preferred path in the cloud computing environment. Firstly, the paper used Preference Degree...
One aim of basic oxygen furnace (BOF) steelmaking endpoint control is the temperature control. For the majority of the china's small or medium BOF, sublance can not be used as a result of restrictions of production conditions, so, researching the BOF endpoint control without sublance has a significant application value. For the data's characteristics of nonlinearity and high noises in the field, a...
Radial basis function (RBF) neural network is increasingly used to predict groundwater table, which often shows complex nonlinear characteristic. But the traditional RBF training algorithm based on gradient descent optimization method can only obtain the partial/local optimums solution sometimes. Furthermore, man-made selecting the structure of RBF neural network has blindness and expends much time...
Groundwater table often shows complex nonlinear characteristic. Radial basis function (RBF) neural network is increasingly used to predict groundwater table. The traditional RBF training algorithm based on gradient descent optimization method can only obtain the partial/local optimums solution sometimes. Furthermore, man-made selecting the structure of RBF neural network has blindness and expends...
A radial basis function neural network model combined with particle swarm optimization algorithm and independent component analysis is proposed, which is used to predict the endpoint of BOF steelmaking. In order to solve the issues that the objective function falls into the local optimum and the sequence of independent components is uncertain, this paper utilizes the global ergodicity of particle...
Time series forecasting is the main method in network flow prediction. RBF neural network is capable of universal approximation, which not only has fast training velocity, but also can solve the local minima problem. Thus, network flow prediction technology based on genetic algorithm and RBF neural network is presented in the paper. And the training parameters are adjusted by genetic algorithm. Network...
Aiming to the features of the load variation of gas pipeline, it is suggested Fuzzy Logic system and RBF Nerve Network Based on Artificial Immune Algorithm is used to predict the load of gas pipeline. The fuzzy logic system is applied to predict the load error and the error variation rate. Then, the RBF Nerve Network Based on Artificial Immune Algorithm is used to predict the load of gas pipeline...
A new disaster monitor and forecast system based on RBF neural networks is proposed. This disaster forecast system consists of disaster spatial monitor subsystem that is pre-trained by off-line learning algorithms and disaster time forecast subsystem developed by online learning algorithms. The disaster spatial monitor subsystem aims to detect trend of the objective behavior, once the unstable condition...
It is of great theoretical and practical significance of the coal and gas outburst prediction. Through the study of the original samples and the example analysis of the Predictable samples, coal and gas outburst prediction based on artificial neural network model is studied. According to the characteristics of coal and gas outburst and coal and gas outburst data indicators, a coal and gas outburst...
A method of Radial Basis Function(RBF)neural network algorithm based on Particle Swarm Optimization (PSO) algorithm is introduced. In the background of PJM electricity market in the USA, the short-term price is forecasted with the historical price and loads. After determining the number, the center and width of the hidden layer, code the weights of output layer to individual particles and optimize...
A target tracking is an important embranchment of WSN, which can assure the position of a moving target real-time. This paper works on the prediction problem of target tracking of chain-type wireless sensor networks. We choose RBF neural network as the basis of the tracking prediction model. Based on analysis of chain-type tracking characters and RBF neural network based tracking prediction model,...
To improve the accuracy of load forecasting, a new algorithm is presented to forecast the short-term load. In the paper, short-time load sequence of the power supply system composed by different frequency signals is decomposed into the signals on different frequency bands by wavelets. Then the Radial Basis Function neural network (RBFNN) is used to forecast these signals in every scale space, and...
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