The adaptive Neural Network Library (Matlab 5.3.1 and later) is a collection of blocks that implement several Adaptive Neural Networks featuring different adaptation algorithms.
It was developed mainly in June-July 2001 by Giampiero Campa (West Virginia University) and Mario Luca Fravolini (Perugia University). Later improvements were partially supported by the NASA Grant NCC5-685.
There are blocks that implement basically these kinds of neural networks:
Adaptive Linear Networks (ADALINE)
Multilayer Layer Perceptron Networks
Generalized Radial Basis Functions Networks
Dynamic Cell Structure (DCS) Networks with gaussian or conical basis functions
Also, a Simulink example regarding the approximation of a scalar nonlinear function is included.
Finally, the file Training.zip includes step by step instrucions on how to train the GRBF network and the supporting example.