Case Study of Learning Entropy for Adaptive Novelty Detection in Solid-Fuel Combustion Control

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 348)

Abstract

This paper deals with the case study of usability of the Learning Entropy approach for the adaptive novelty detection in MIMO dynamical systems. The novelty detection is studied for typical parameters of linear systems including time delay. The solid-fuel combustion process is selected as a representative of typical non-linear dynamic MIMO system. The complex mathematical model of a biomass-fired 100kW boiler is used for verification of the potentials of the proposed method, and the motivation for novelty detection in solid-fuel combustion processes is discussed in this paper.

Keywords

adaptive systems novelty detection learning entropy combustion process multiple input control incremental learning quadratic neural unit 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Instrumentation and Control EngineeringCzech Technical University in PraguePragueCzech Republic

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