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.
Keywords for this software
References in zbMATH (referenced in 3 articles )
Showing results 1 to 3 of 3.
- Zain, Azlan Mohd; Haron, Habibollah; Qasem, Sultan Noman; Sharif, Safian: Regression and ANN models for estimating minimum value of machining performance (2012)
- Ekici, Betul Bektas; Aksoy, U.Teoman: Prediction of building energy consumption by using artificial neural networks (2009)
- Fan, Wei; Yuan, Wancheng; Fan, Qiwu: Calculation method of ship collision force on bridge using artificial neural network (2008)