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This paper optimizes the wavelet neural networks with genetic algorithms which has the optimization of the overall search capabilities, and establishes the model of wavelet neural networks based on genetic algorithms. It overcomes the shortcomings of BP neural network for their own, and it can get higher accuracy and faster convergence. The examples also show that the model can improve forecast accuracy...
According to the low sample and multifactor impact for long-medium term power load forecasting, the grey relational grade was used in screening factors, the combined model in BP neural network and SVM was established, and the multivariate variables and history load variables were used to roll prediction. The combined predictive values are obviously better than single method. Empirical study showed...
An improved BP Neural Network with additional momentum and adaptive learning is proposed in the paper to predict the growth rate of electricity consumption in China. Matlab7 is used as modeling tool to design the model. Current year GDP growth, electric power consumption growth and growth rate of secondary industry are taken as input variables while next year electric power consumption growth is predicted...
The paper combines rough sets and ANN to analyze inventory early-warning in supply chains. The introduction of Rough sets cuts down the input dimensions of ANN, and the ANN algorithm is improved by adding the momentum factor mc and applying adaptive learning rate. Lastly, according to the inventory data of a manufacturing enterprise in Handan City, the paper proves the validity of the proposed model.
As the power load forecasting sequence has stochastic growth and nonlinear wave characteristics, grey neural network model can effective reflect the growth properties of the sequence and fit the nonlinear relation. Markov chain can easily embody the random characteristic of system by complex factors, so the Markov chain error correction method was introduce in this paper, the whole forecasting precision...
According to the low sample and multifactor impact for long-medium term power load forecasting, the grey relational grade was used in screening factors, the combined model in BP neural network and SVM was established, and the multivariate variables and history load variables were used to roll prediction. The combined predictive values are obviously better than single method. Empirical study showed...
Because power loads are influenced by various factors, and the changes of power load presents are complicate, the traditional forecasting methods are always not satisfied. According to the random-increase and non-linearity fluctuation of residual series, gray neural network forecasting can reflect the increase character and non-linearity relationship. This paper using the improved ACO method as the...
The paper adopts rough set reduction algorithm to reduce the influence factors of power plant selection and eliminate the uncorrelated attribution, through which we can obtain typical samples. After this, adopting fuzzy method to calculate the membership degree of the typical samples, which are looked on as the input of BP Neural Network and the expert values are as the expected output to train the...
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