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Generalized Maximum Correntropy-Based Echo State Network for Robust Nonlinear System Identification

机译：基于广义最大熵的回波状态网络的鲁棒非线性系统辨识

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摘要

In this paper, we propose a robust method for non-linear system identification that incorporates robustness to echo state networks (ESNs). In particular, the ESNs utilize generalized correntropy as a loss function to get optimal solutions. Generalized correntropy is a more flexible extension of correntropy in information theoretic learning (ITL). Generalized correntropy induced metric (GCIM) is robust to outliers with a proper shape parameter. The ESNs with GCIM can provide the anti-noise capacity and are insensitive outliers which are prevalent in real-world tasks. They also inherit the basic architecture of echo state network but replaces the commonly used mean square error (MSE) criterion with GCIM. The stochastic gradient descent method is adopted to optimize the generalized correntropy-based cost function. Numerical simulations are given to show that the proposed algorithm is robust to the non-Gaussian noise and outliers.
机译：在本文中，我们提出了一种用于非线性系统识别的鲁棒方法，该方法将鲁棒性结合到回波状态网络（ESN）中。特别地，ESN利用广义的熵作为损失函数来获得最佳解。广义熵是信息理论学习（ITL）中熵的更灵活扩展。广义的熵诱导度量（GCIM）对于具有适当形状参数的异常值具有鲁棒性。具有GCIM的ESN可以提供抗噪能力，并且是在现实世界中普遍存在的不敏感异常值。它们还继承了回波状态网络的基本体系结构，但用GCIM代替了常用的均方误差（MSE）准则。采用随机梯度下降法对广义基于熵的成本函数进行优化。数值仿真表明，该算法对非高斯噪声和离群值具有鲁棒性。

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