Instantaneous anomaly detection in online learning fuzzy systems

In the field of self-optimizing automation systems, incremental local learning is an important technique. But especially in case of closed loop coupling, learnt anomalies may have a negative influence on the entire future of the evolving system. In the worst case, this may result in unstable or chaotic system behavior. Thus it is crucial to detect anomalies in online learning systems instantaneously to be able to take immediate counteractions. This paper presents an intuitive approach how to detect anomalies in incrementally and locally learning TS-fuzzy systems by looking at local meta-level characteristics of the learnt function. The practical feasibility of this approach is then investigated in experiments with a real pole-balancing cart.