Nonlinear System Identification using Spatio-Temporal RBF-NN
Herein, you will find three variants of radial basis function neural network (RBF-NN) for nonlinear system identification task. In particular, I implemented RBF with conventional and fractional gradient descent, and compared the performance with spatio-temporal RBF-NN.
* For citations see [cite as] section
Zitieren als
Shujaat Khan (2024). Nonlinear System Identification using Spatio-Temporal RBF-NN (https://www.mathworks.com/matlabcentral/fileexchange/68415-nonlinear-system-identification-using-spatio-temporal-rbf-nn), MATLAB Central File Exchange. Abgerufen .
Khan, Shujaat, et al. “A Novel Adaptive Kernel for the RBF Neural Networks.” Circuits, Systems, and Signal Processing, vol. 36, no. 4, Springer Nature, July 2016, pp. 1639–53, doi:10.1007/s00034-016-0375-7.
Khan, Shujaat, et al. “A Fractional Gradient Descent-Based RBF Neural Network.” Circuits, Systems, and Signal Processing, vol. 37, no. 12, Springer Nature America, Inc, May 2018, pp. 5311–32, doi:10.1007/s00034-018-0835-3.
Khan, Shujaat, et al. “Spatio-Temporal RBF Neural Networks.” 2018 3rd {IEEE} International Conference on Emerging Trends in Engineering, Sciences and Technology ({ICEEST}), {IEEE}, 2018
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Nonlinear_System_Identification
Version | Veröffentlicht | Versionshinweise | |
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1.1.2 | - update citation information |
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1.1.1 | - title change |
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1.1 | - Comparison with conventional and fractional variant |
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1.0.2 | - Simplification of code syntax |
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1.0.1 | - Example added |
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1.0.0 |