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.
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References in zbMATH (referenced in 5 articles )
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- Fan, Wei; Yuan, Wancheng; Fan, Qiwu: Calculation method of ship collision force on bridge using artificial neural network (2008)