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In this paper, we analyze a cooperative medium access scheme in a wireless relaying network using Bayesian games, where the participating nodes are peers subject to the half-duplex constraint and they choose to cooperate or not cooperate based on its expected utility. We first set up a one-stage game and derive the ex-post utility. A two-stage game with incomplete information is further formulated...
In terms of the Particle Swarm Optimization-Neural Network (PSO-NN), a new prediction model has been developed using the stepwise regression method combined with the feature extraction technique of Isometric Mapping (ISOMAP) algorithm to treat the Climatology and Persistence (CLIPER) predictors. The model is validated with forecasts of ten years of typhoon intensity formed and numbered in the Western...
Based on the numerical forecast products of T213 and Japan, a new nonlinear rainstorm prediction model is developed for local heavy rain. The Japanese rainfall forecast products is used to distinguish the likelihood of heavy rain 24 hours later. Then the Chebyshev sliding nested expansion is applied to the forecast field by T213 for forecast factors best correlated with the series of rainfall. And...
A nonlinear prediction model has been presented of PSO-ANN of monthly precipitation in rain season. It differs from traditional prediction modeling in the following aspects: (1) input factors of the PSO-ANN model of monthly precipitation were selected from a large quantity of preceding period high correlation factors, and they were also highly information-condensed by using the empirical orthogonal...
A new calculation method for the input of the neural network ensemble prediction (NNEP) model has been developed based on the data mining technology using the feature extraction method of Empirical Orthogonal Function(EOF) and the stepwise regression method, for investigating the effect of different model input with the same dimension on the prediction capacity of the NNEP model. Taking typhoon intensity...
Taking the mean precipitation from 16 stations spread around the south China during the pre-flood season as the prediction object treated by Empirical Orthogonal Function (EOF) method, previous physical predictors and factors that reflected the significant period of predictands by means of the Mean Generating Functions (MGF) technique, were extracted useful information for prediction by using Partial...
Empirical risk minimization based neural network suffers drawbacks like over fitting the training data and the choice of the topology structure. According to the periodicity and trend of precipitation, the precipitation forecast model based on support vector machine (SVM) was developed. SVM possesses high generalization ability by employing structural risk minimization to minimize the learning errors...
To improve the predictive ability of a fuzzy neural network prediction model, the re-selection is made, by means the rough set attribute reduction, of the correlated prognostic factors that have been chosen and the re-selected factors are treated by blurring as model input, thereby establishing a new-type fuzzy neural network predictive model. Experiments are conducted for approximately two months...
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