Reinforcement learning with Takagi-Sugeno-Kang fuzzy systems
Eric Zander, Ben van Oostendorp, Barnabás Bede
Abstract
Complex Engineering Systems are generally represented in terms of a set of interconnected systems that their collective/global behaviors/properties are somewhat difficult to be predicted or managed. The context of complex engineering systems is mainly concerned with developing multi-component engineering systems, designs, or algorithms to exploit those unpredictable collective/global behaviors/properties. Complexity in Engineering Systems is in general manifested in component, product, system, interconnections of interacting subsystems or multidisciplinary system designs. In a broad sense, complexity is related to the expected amount of information may need to describe a dynamical system.
Topics & Concepts
Reinforcement learningReinforcementFuzzy logicPsychologyArtificial intelligenceComputer scienceCognitive psychologySocial psychologyFuzzy Logic and Control Systems