The paper presents a novel quantum neural network (QNN) model with variable selection for short term load forecasting. In the proposed QNN model, first, the combiniation of maximum conditonal entropy theory and principal component analysis method is used to select main influential factors with maximum correlation degree to power load index, thus getting effective input variables set. Then the quantum neural network forecating model is constructed. The proposed QNN forecastig model is tested for certain province load data. The experiments and the performance with QNN neural network model are given, and the results showed the method could provide a satisfactory improvement of the forecasting accuracy compared with traditional BP network model.
With the development of communication technology, the increasing scale of power communication network is bigger and bigger. Based on the important significance of electric power communication network to establish an alarm information acquisition system is necessary. CORBA, as a distributed processing environment interconnect solutions in hardware and software, is widely used in the field of network management for its high versatility. In this paper we use real-time CORBA platform TAO as a network management platform to develop a kind of electric power communication network alarm acquisition system.
In order to find a more accurate center wavelength detection fitting algorithm for fiber Bragg grating (FBG) reflection spectrum, Based on the theory of neural network, we designed different neural network to study and fit the spectral data which has been acquired from FBG.In this paper,we have studied BP,RBFand wavelet neural network structure,comparing the network training time and the result of fitting curves ,wavelet neural network is proved to be the best fitting algorithm.
In this paper, we focus on the wide-range temperature dependence of Brillouin shift in an optical fiber. We set about our study on the basis of the material properties of fused silica, including the Young's modulus, the Poisson's ratio, the refractive index, the density and the thermal expansion coefficient. We have built up mathematical model for the temperature dependence of each of the material properties according to the experimental data reported previously in some literatures. Based on the relationship between Brillouin shift and the material properties, the Brillouin shift has been formulized as a second-order polynomial of temperature over a wide temperature range. The temperature dependence of Brillouin shift in a commercially available dispersion-shifted fiber has been experimentally investigated from room temperature up to 820 'C. A comparison between the theoretical and the experimental results shows that they are in good agreement with each other.
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