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In this paper, we present a model based on the Neural Network (NN) for classifying Arabic texts. We propose the use of Singular Value Decomposition (SVD) as a preprocessor of NN with the aim of further reducing data in terms of both size and dimensionality. Indeed, the use of SVD makes data more amenable to classification and the convergence training process faster. Specifically, the effectiveness...
This paper proposes a new global optimization technique in which combines population migration algorithm (PMA) and radial basis function (RBF) neural networks learning algorithm for training RBF neural network. Compared with the traditional RBF training algorithm, the simulation results show that the method has a higher accuracy in a stringency and works well in avoiding sticking in local minima.
There is a gap between the theoretical results of regularization theory and practical suitability of regularization-derived networks (RN). On the other hand, radial basis function networks (RBF) that can be seen as a special case of regularization networks, have a rich selection of learning algorithms. In this work we study a relationship between RN and RBF, and show that theoretical estimates for...
Lots of growing neural network models have been proposed to tackle the incremental learning problem, but they also bring about the problem of fast growing complex structure. In this paper, we present a combinational Neural Network of SOM (Self-Organizing Maps) and RBF (Radial Basis Function) based on incremental learning method. The experiment of acoustic fault sources identification of underwater...
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