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Chen Y.,Jiyuan Vocational and Technical College
IET Conference Publications | Year: 2012

According to the questions of Radial Basis Function (RBF) neural network in the mechanical failure diagnose of high voltage breakers, which can extremely affect convergence speed and precision of the RBF neural networks. This paper develops an improved RBF neural network learning algorithm based on immune algorithm. In the algorithm, the input data are regarded as antigens and the compression mapping of antigens as antibodies, i.e., the concealed layer center point, which also avoid network concealed layer center point hard problem. The weights of the output layer are determined by adopting the gradient descent algorithm. Then it imposes discipline good network on the mechanical failure diagnose of high voltage breakers. The simulation results indicate that this method has preferable application value in the mechanical vibration signal of high voltage breakers. Source


Zhang Y.,Jiyuan Vocational and Technical College
Key Engineering Materials | Year: 2011

Along with the economy development and the living standards enhancement unceasingly, the inflation also accompanies naturally lives. Generally speaking the temperate inflation has not the enormous influence to people's life and the national economy, but the serious inflation's occurrence can affect a country exchange rate level, disrupt the import and export order and financial order, simultaneously, will affect the people revenues and the living standard, will cause the national competitive power to drop. In view of this problem, it is necessary to construct the model to forecast the year in the future that will occur the serious inflation. This paper construct the predict model base on the grey fuzzy theory, the experimental simulation is shown that the results is accuracy and reliable. © (2011) Trans Tech Publications. Source


Zhou H.-Y.,Jiyuan Vocational and Technical College
Xiandai Huagong/Modern Chemical Industry | Year: 2014

The desulfurization of the simulated oil(S content 800 ppm)consisting dibenzothiophene and n-octane is performed by oxidation-extraction coupling method with [C16H33N(CH3)3]3PMo12O40 as catalyst, H2O2 as oxidant and [BMIM]BF4 as extraction solvent. The effects of the oxidation time, the oxidant and catalyst dosage, the temperature and the reaction mechanism on oxidation-extraction coupling desulfurization are investigated. The results show that the removal rates of DBT can reach 95.1% under the following conditions: 120 minutes of the oxidation time, 60℃ of the temperature, 0.05 of n(catalyst)/n(S), 4 of n(H2O2)/n(S and 1 m L of [BMIM]BF4. The system [C16H33N-(CH3)3]3PMo12O40/H2O2/[BMIM]BF4 could be recycled for five times without the obvious decrease in activity. ©, 2014, China National Chemical Information Center. All right reserved. Source


Zhang Y.,Jiyuan Vocational and Technical College
3rd International Conference on Knowledge Discovery and Data Mining, WKDD 2010 | Year: 2010

For a long time, the necessary funds of entire electric power industry is fully funded by the government or mandatory financial loans due to the monopoly of the electric power industry and the government acts, and which results in the research on the electric power enterprise financing credit capacity (FCC) evaluation is lacking. The financing capacity is influenced by many factors, including the qualitative indicators and quantitative indices, this paper overcomes the shortcoming of tradition linear evaluation methods of financing credit capacity, proposes a measuring method which establishes a capacity evaluation system combined with Kirkpatrick model and describes the evaluation mechanism based on fuzzy neural network (FNN) algorithm. The capacity evaluation of 10 enterprises shows that the results given by this model are reliable, and this method to evaluate the financing credit capacity is feasible. © 2010 IEEE. Source


Zhou H.,Jiyuan Vocational and Technical College
Zeitschrift fur Kristallographie - New Crystal Structures | Year: 2015

C11H9N5, monoclinic, P21/c (no. 14), a = 14.15(1) Å, b = 3.783(3) Å, c = 18.09(2) Å, β = 92.28(2)°, V = 968.0 Å3, Z = 4, Rgt(F) = 0.0517, wRref(F2) = 0.1504, T = 296 K. © 2015 Walter de Gruyter GmbH, Berlin/Munich/Boston. Source

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