Self-learning and neural network adaptation by embedded collaborative learning engine (eCLE) — An overview

This paper provides an overview of a novel scheme for constructing machine evolutionary behavior within systems. Specifically, evolving learning for the autonomous recognition of both known as well as newly emerging behaviors is provided. The paper is related with several open research problems such as cognition, incremental learning, and self-learning within the context of health monitoring systems (fault diagnosis and prognosis). Also, it is addressed the need for a formal methodology and its implementation for adding new knowledge, thus enabling the automated recognition of new patterns (e.g. behaviors) within systems. A key feature of the resulting embedded Collaborative Learning Engine (eCLE) when generating machine evolutionary behavior consists of operating with an ensemble of learning paradigms, which when instantiated work in a collaborative way. The resulting framework not only compiles the inherent advantages of the involved methods, but also enables synergistic behavior by working in a collaborative fashion.