Nonlinear System Identification using RBF Neural Network
In this simulation I implemented an RBF-NN for the zero order approximation of a nonlinear system. The simulation includes Monte Carlo simulation setup and the RBF NN code. For system estimation Gaussian kernels with fixed centers and spread are used. Whereas, the weights and the bias of the RBF-NN are optimized using the gradient descent-based adaptive learning algorithm.
Citation:
Khan, S., Naseem, I., Togneri, R. et al. Circuits Syst Signal Process (2017) 36: 1639. doi:10.1007/s00034-016-0375-7
https://link.springer.com/article/10.1007/s00034-016-0375-7
Citation pour cette source
Shujaat Khan (2024). Nonlinear System Identification using RBF Neural Network (https://www.mathworks.com/matlabcentral/fileexchange/66322-nonlinear-system-identification-using-rbf-neural-network), MATLAB Central File Exchange. Récupéré le .
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- AI and Statistics > Deep Learning Toolbox > Function Approximation, Clustering, and Control > Function Approximation and Clustering >
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Inspiré par : Function approximation using "A Novel Adaptive Kernel for the RBF Neural Networks", Mackey Glass Time Series Prediction using Radial Basis Function (RBF) Neural Network
A inspiré : Nonlinear System Identification using Spatio-Temporal RBF-NN
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