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On the side of enhancing the execution of skills, specialists in sports are adopting analysis of kinematics to correct actions of an athlete. By means of technological resources used to measure physical variables and to supply relevant data to trainers, results related to improvements on athletes' performance are being achieved. In this context, this work uses the Radial Basis Function Neural Networks...
Time Series Prediction is widely used in our daily life. We propose a forecasting method based on RBF neural network for time series prediction in this paper. This approach consists of two phases, training phase and working phase. During training phase, we integrate subtractive clustering method and k-means method to generate the centers of RBF neural network, which can cover the shortage of only...
A way of combining SVM(Support Vector Machine) with Supervised Subset Density Clustering is proposed in this paper. How to minimize the training set of SVM by means of clustering is researched. Original center positions are of great importance to clustering accuracy. However the traditional clustering center choosing algorithm doesn't work properly when the same kind of samples aren't closely-spaced...
According to some flaws in the existing topic tracking methods, a new method of self-adaptive emergency topic tracking model based on CHI_LDA and timing characteristics is proposed in this paper. Apply the CHI_LDA method to establish the model for the news topics and reports, not only resolving the problems of high dimension and sparseness in the feature space and semantic relevance, but also improving...
To resolve the key issue of uncertainty knowledge representation in the process of establishing threat assessment system, a variable-parameter dynamic Bayesian network for threat assessment is proposed. A self-adaptive assessment system based on variable-parameter dynamic Bayesian network is presented. And a novel threat assessment optimization algorithm based on variable-parameter dynamic Bayesian...
This paper addresses the questions of improving convergence performance for back propagation (BP) neural network. For traditional BP neural network algorithm, the learning rate selection is depended on experience and trial. In this paper, based on Taylor formula the function relationship between the total quadratic training error change and connection weights and biases changes is obtained, and combined...
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