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This submission is a part of a paper titled as "A Novel Adaptive Kernel for the RBF Neural Networks". In this simulation I implemented four different kernels of RBF for a pattern classification problem, namely (1) Cosine kernel, (2) Euclidean/Gaussian kernel, (3) Novel kernel (Manual fusion of Cosine and Euclidean and Gaussian, and A Novel Adaptive kernel for RBF [2].
[1] Wasim Aftab, Muhammad Moinuddin, and Muhammad Shafique Shaikh, “A Novel Kernel for RBF Based Neural Networks,” Abstract and Applied Analysis, vol. 2014, Article ID 176253, 10 pages, 2014. doi:10.1155/2014/176253
https://www.hindawi.com/journals/aaa/2014/176253/cta/
[2] 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
Cite As
Shujaat Khan (2026). Adaptive Novel Kernel for RBF Neural Networks (Pattern Classification Problem) (https://www.mathworks.com/matlabcentral/fileexchange/63556-adaptive-novel-kernel-for-rbf-neural-networks-pattern-classification-problem), MATLAB Central File Exchange. Retrieved .
Acknowledgements
Inspired: Function approximation using "A Novel Adaptive Kernel for the RBF Neural Networks"
General Information
- Version 1.0.0.0 (1.09 MB)
MATLAB Release Compatibility
- Compatible with any release
Platform Compatibility
- Windows
- macOS
- Linux
| Version | Published | Release Notes | Action |
|---|---|---|---|
| 1.0.0.0 |
