Efficient Real-Time Continuous Classification Learning and Fault-Tolerant Computing
Meng-Lai Yin · 2024
Digital engineering ecosystems rely heavily on reliable data processing, where machine learning is commonly applied. This paper addresses the perspective that Reliability and Maintainability (R&M) engineering and machine learning go hand in hand, through multi-dimensional classification learning and fault-tolerant computing. While machine learning can enhance fault-tolerant computing, reliability techniques can improve the accuracy of multi-dimensional classification learning. Algorithms developed are time efficient, allowing them to be applied to real-time application. Case studies demonstrate the efficacy of the methods.