Robust Fault-Aware Extreme Learning Machine Based on Maximum Correntropy.

Abstract

Extreme learning machine (ELM) is an effective and efficient neural model for universal approximation. However, its practical performance can degrade due to weight noise, node faults, and outliers. This brief introduces a robust ELM algorithm designed to address these issues and enhance network robustness. We first analyze the square error of the classic ELM, considering both weight noise and node faults. By integrating an outlier-resistant method, the maximum correntropy criterion (MCC), we derive a new objective function to bolster network resilience. This leads to the development of the robust fault-aware ELM (RFAELM) algorithm. The convergence property of RFAELM is rigorously proven. For validation, the proposed algorithm is evaluated in various noise and fault levels using eight different benchmark datasets. The simulation results, encompassing all imperfect conditions and datasets, verify the robustness and generalization of this new algorithm. Also, the new algorithm is compared with other robust ELM algorithms using different statistical measurements. The superior performance of RFAELM substantiates its significant improvement over existing algorithms.