Nonlinear System Identification using RBF Neural Network

版本 1.0.0.0 (47.3 KB) 作者: Shujaat Khan

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更新时间 2018/3/5

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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

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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. 检索来源 2024/4/26.

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参考作品: Function approximation using "A Novel Adaptive Kernel for the RBF Neural Networks", Mackey Glass Time Series Prediction using Radial Basis Function (RBF) Neural Network

启发作品: Nonlinear System Identification using Spatio-Temporal RBF-NN

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System_Identification_RBF

版本	已发布	发行说明
1.0.0.0	2018/3/5		下载

Nonlinear System Identification using RBF Neural Network