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Short-term load in power system is nonlinear and non-stationary. To cope with the problem that the training error of neural network prediction model is increased while the generalization ability is reduced caused by the large input fluctuation, the rough neurons with upper and lower inputs are introduced into the radial basis function (RBF) neural networks, a power system short-term load forecasting...
As the important power equipment in the mechanical system, fault diagnosis for asynchronous motor is helpful to monitor working status and prevent failure causing unnecessary loss. In the fault diagnosis domain, feature extraction is the key step which is related to the performance of diagnosis results. For the asynchronous motor, the motor current signature analysis (MCSA) is one of the most powerful...
Although polarization and spectral information utilization has been received great attention with the sensor and detection technology advance, few results are showed to jointly utilize both of this information in targets classification. Polarization and spectral information reveals two different aspects of one single target, and therefore, if both of information is properly used, good performance...
Kinds of text opinion on the Internet are very important to the development of companies, however, methods of opinion mining is very limited, because there always need to deal with many meaning analysis, so this paper presents a new method on opinion mining based on danger theory. Using this method, companies can make decision easier.
Chinese word segmentation is an important foundation for Chinese information processing. This paper proposes a new Chinese word segmentation model based on Bayesian network. In this model, Character alignment Viterbi algorithm, which treats the preceding word of each Chinese character as its state, and the N-gram probability as its state transition probability, is suggested to be combined with Viterbi...
A new RBF neural network is presented to overcome the shortcoming that the training process of RBF neural network is slow in the paper. The immune genetic algorithm is combined with the RBF neural network to optimize the center of the RBF network, improve the definition method of affinity degree, and introduce adjusting factor based on density. Thus the learning efficiency and approximation precision...
Least squares support vector machines (LSSVM), as a recently reported least squares version support vector machines (SVM), involves equality constraints instead of inequality constraints and adopts least squares cost function, therefore it expresses the training by solving a set of linear equations instead of the quadratic programming problem which greatly reduces computational cost. In this paper,...
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