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http://dx.doi.org/10.5762/KAIS.2021.22.5.561

Adaptive Kernel Estimation for Learning Algorithms based on Euclidean Distance between Error Distributions  

Kim, Namyong (Division of Electronic, Information & Communication Eng., Kangwon University)
Publication Information
Journal of the Korea Academia-Industrial cooperation Society / v.22, no.5, 2021 , pp. 561-566 More about this Journal
Abstract
The optimum kernel size for error-distribution estimation with given error samples cannot be used in the weight adjustment of minimum Euclidean distance between error distributions (MED) algorithms. In this paper, a new adaptive kernel estimation method for convergence enhancement of MED algorithms is proposed. The proposed method uses the average rate of change in error power with respect to a small interval of the kernel width for weight adjustment of the MED learning algorithm. The proposed kernel adjustment method is applied to experiments in communication channel compensation, and performance improvement is demonstrated. Unlike the conventional method yielding a very small kernel calculated through optimum estimation of error distribution, the proposed method converges to an appropriate kernel size for weight adjustment of the MED algorithm. The experimental results confirm that the proposed kernel estimation method for MED can be considered a method that can solve the sensitivity problem from choosing an appropriate kernel size for the MED algorithm.
Keywords
Adaptive kernel size; MED; Error distribution; Delta function; Euclidean distance; Averaged error power;
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