Abstract
RBF neural network have advantages of training simple, fast efficiency of learning, easy to fall into local minima, etc..It is widely used to solve the problem in signal processing and pattern recognition. Although the common RBF network is relatively easy to build, but because of the structure is usually fixed or high complexity, resulting in learning time is too long or network resource waste. For these reasons, proposed using extended Kalman filter as the RBF learning algorithm, and using double radial function in the hidden layer. By approaching the basis of the results of the analysis clearly shows that the network model than the other categories have a stronger generalization.
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