Radial basis function network: Difference between revisions

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Network architecture: c_i -> \mathbf c_i
Training: c_i -> \mathbf c_i
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==Training==
 
In a RBF network there are three types of parameters that need to be chosen to adapt the network for a particular task: the center vectors <math>\mathbf c_i</math>, the output weights <math>w_i</math>, and the RBF width parameters <math>\beta_i</math>. In the sequential training of the weights are updated at each time step as data streams in.
 
For some tasks it makes sense to define an objective function and select the parameter values that minimize its value. The most common objective function is the least squares function