Adaptive Fusion of Kernels for Radial Basis Function Neural Network

Versione 1.0.0.0 (1,03 MB) da Shujaat Khan
Simulation of adaptive fusion of two kernels of RBF for pattern recognition example
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Aggiornato 4 set 2016

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In this algorithm the two popular similarity measures, Cosine distance (angle) and Euclidean distance are fused together and the mixing weight is made adaptive using gradient decent algorithm. The submission is the example for pattern recognition problem utilized in the paper [1].
Reference
[1] http://link.springer.com/article/10.1007/s00034-016-0375-7
% @article{khan2016novel,
% title={A Novel Adaptive Kernel for the RBF Neural Networks},
% author={Khan, Shujaat and Naseem, Imran and Togneri, Roberto and Bennamoun, Mohammed},
% journal={Circuits, Systems, and Signal Processing},
% pages={1--15},
% year={2016},
% publisher={Springer US}
% }

Cita come

Shujaat Khan (2024). Adaptive Fusion of Kernels for Radial Basis Function Neural Network (https://www.mathworks.com/matlabcentral/fileexchange/59001-adaptive-fusion-of-kernels-for-radial-basis-function-neural-network), MATLAB Central File Exchange. Recuperato .

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Pattern_Recognition_Using_NAK_RBF/

Versione Pubblicato Note della release
1.0.0.0