Speeding-up adaptive heuristic critic learning with FPGA-based unsupervised clustering

Neurocontrol is a crucial area of fundamental research within the neural network field. Adaptive heuristic critic learning is a key algorithm for real-time adaptation in neurocontrollers. In this paper, we show how an unsupervised neural network model with an adaptable structure can be used to speed-up adaptive heuristic critic learning, present its FPGA design, and show how it adapts the neurocontroller to the state space of the system being controlled.