Implementation of Neural Network Learning with Minimum L1-Norm Criteria in Fractional Order Non-gaussian Impulsive Noise Environments

Authors:
Daifeng Zha
Affiliations:
College of Electronic Engineering, Jiujiang University, Jiujiang, China 332005
Venue:
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks
Year:
2008

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Abstract

Minimum L1-norm optimization model has found extensive applications in linear parameter estimations. L1-norm model is robust in non Gaussian alpha stable distribution error or noise environments, especially for signals that contain sharp transitions (such as biomedical signals with spiky series) or dynamic processes. However, its implementation is more difficult due to discontinuous derivatives, especially compared with the least-squares (L2-norm) model. In this paper, a new neural network for solving L1-norm optimization problems is presented. It has been proved that this neural network is able to converge to the exact solution to a given problem. Implementation of L1-norm optimization model is presented, where a new neural network is constructed and its performance is evaluated theoretically and experimentally.