Abstract
To increase performance of Radial Basis Functions (RBFs) in Artificial Neural Network q-Gaussian Radial Basis Function (q-GRBF) is introduced to optimize singular value of Y, Cb and Cr color image components. Various radial basis functions such as Gaussian Radial Basis Function (GRBF), Multi Quadratic Radial Basis Function (MCRBF), Inverse Multi Quadratic Radial Basis Function (IMCRBF) and Cosine Radial Basis Function (CRBF) are also introduced and compared with singular values of Y, Cb and Cr component of color images. Simulation and analysis shows that q-Gaussian Radial Basis Function gives lesser error and better result compared to the other radial basis functions in artificial neural network.
Published Version
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