Research on lifelong learning method for intelligent diagnosis of rail transit equipment

Abstract

With the gradual maturity of statistical machine learning, it is now time to break the traditional model of isolated learning and turn to lifelong learning, pushing machine learning to a new level. Applications such as intelligent assistants, chat robots, and physical robots are also in urgent need of this lifelong learning ability. Without the ability to accumulate knowledge and use it to gradually learn more, a system may never be truly intelligent. Continuous learning algorithms attempt to achieve the same capabilities for neural networks and solve catastrophic forgetting problems. Therefore, in essence, continuous learning performs incremental learning for new tasks.