Novelty Detection in System Monitoring and Control with HONU

With focus on Higher Order Neural Units (HONUs), this chapter reviews two recently introduced adaptive novelty detection algorithms based on supervised learning of HONU with extension to adaptive monitoring of existing control loops. Further, the chapter also introduces a novel approach for novelty detection via local model monitoring with Self-organizing Map (SOM) and HONU. Further, it is discussed how these principles can be used to distinguish between external and internal perturbations of identified plant or control loops. The simulation result will demonstrates the potentials of the algorithms for single-input plants as well as for some representative of multiple-input plants and for the improvement of their control.