Realization of the RBF network--the return of nonl
2016-08-23
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In 1985, Powell multivariate interpolation method of radial basis function (RadicalBasis Function). In 1988 Moody and Darken a neural network structure is proposed, namely RBF Neural network, which belongs to the feedforward neural network type, it can be arbitrary-precision approximation of arbitrary functions. RBF network structure and multilayer feedforward neural network is similar, is a three-layer network. Input signal source node comprise second hidden layer, needs of the number of hidden units depending on the description of the problem, and hidden cells transform function RBF () is the central point of nonnegative radial symmetry and attenuation of nonlinear functions; a third layer to output layer, it responds to input mode. Enter a space to hide layer space transform is a linear, from the hidden layer space transform is a linear output layer space.
RBF network's basic idea is: using RBF as hidden units in the "matrix" constitutes a hidden layer of space, so th
RBF network's basic idea is: using RBF as hidden units in the "matrix" constitutes a hidden layer of space, so th
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