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Inspired by feature selection problems in machine learning and statistics, we study classification problems where the label function depends only on an unknown low dimensional relevant subspace of the data (we call this a k-subspace junta). Assuming that the relevant subspace is truly independent of the irrelevant subspace, and that the distribution over the irrelevant subspace is Gaussian, we give...
Blind equalization by neural network has two difficult problems, which is convergence rate and computational complexity. Resilient BP algorithm (RPROP) combining compressed transfer function is proposed to improve blind equalization by neural network. Compressed transfer function can make the input signal avoid saturation zone and RPROP algorithm can improve convergence rate effectively without adding...
Blind equalization based on compensation fuzzy neural network has proposed. It using compensation neurons to get tradeoff between negative and active operation, thus the network can be training using fuzzy rule of initial right or wrong setting, and fault tolerance and stability are improved. Simulations results show that this algorithm has faster convergence speed and less residual error compared...
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