Interactive gradient algorithm for radial basis function networks

Li, Jianyu; Luo, Siwei; Qi, Yingjian; Huang, Yaping

doi:10.1109/icosp.2002.1180002

Cited by 3 publications

(1 citation statement)

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“…[7] Proposed a reformulated radial basis function is used gradient descent training with supervised learning. [8] Presented the new learning strategy involving not only local optimization of variances of activation function but also global optimization called as interactive gradient learning method. [9] Developed a stochastic search learning algorithm which proved to be better algorithm than back propagation error learning for the recurring artificial neural network.…”

Section: Related Workmentioning

confidence: 99%

Unified Noise Reduction using Adaptive Radial Basis Function

Jeelani¹,

B²

2019

IJITEE

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The images captured by SAR and sonar are blurred and corrupted more by speckle noise and also other types of noise like Gaussian noise and salt & pepper noise. Denoising all types of noises to get perfect image is a vital challenge, earlier works on the same mode addressed with one filter for one noise, there is no one common or unified filter which can denoise all types of noise. Therefore in this paper, we have designed a filter which not only removes speckle noise, but also combination of other noises. Here IUNR (Intelligent Unified Noise Reduction) algorithm is proposed which is based on neural network called adaptive radial basis function acts as a unified filter for Denoising. Proposed method needs a single noisy image to train the adaptive radial basis function neural network to learn the correction of the noisy image. The Gaussian kernel function is applied to reconstruct the local disturbance appeared because of the noise. The proposed adaptive radial basis function network is compared with the fixed form which has fixed spread and the center value of kernel function. This method can correct the image suffered from different varieties of noises like speckle noise, salt & pepper noise and Gaussian noise separately or combination of noise. Various standard test images are considered for test purpose with different levels of noise density and performance of proposed algorithm is compared with adaptive wiener filter.

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Section: Related Workmentioning

confidence: 99%