RBF networks

Why RBF networks ?
As the foregoing illustration shows, every region of the feature space is covered only by a small number of gaussians.

RBF networks are trained by
deciding on how many hidden units there should be
deciding on their centres and the sharpnesses (standard deviation) of their Gaussians
training up the output layer.

RBF networks have a number of advantages over MLPs. First, as previously stated, they can model any nonlinear function using a single hidden layer, which removes some design-decisions about numbers of layers.

Deriving fuzzy rules from trained RBF networks.
Fuzzy logic based tuning of neural network training parameters.
Fuzzy logic criteria for increasing a network size.

7 Hybrid Learning Procedure for RBF Networks 249
5.8 Computer Experiment: Pattern Classification 250
5.9 Interpretations of the Gaussian Hidden Units 252
5.10 Kernel Regression and Its Relation to RBF Networks 255
5.11 Summary and Discussion 259 ...

For other networks that attempt to add complexity look at RBF networks, GRNN (general regression neural networks), FANRE, and I think pi-sigma, and other kernel based neural networks.
4 ...

Gaussians with three different standard deviations. Training RBF Networks. RBF networks are trained by ...

HiRBF cascading together two RBF networks, where both network have different structure but using the same algorithms.

See also: Neural network, Artificial intelligence, Classification, Multilayer Perceptron, Perceptron