A SELF-IMPROVING FUZZY CEREBELLAR MODEL ARTICULATION CONTROLLER WITH STOCHASTIC ACTION GENERATION
Kao‐Shing Hwang, Yuan-Pao Hsu · Cybernetics & Systems · 2002
A modified fuzzy cerebellar model articulation controller (FCMAC) with reinforcement learning capability is introduced in this article. This model utilizes the likelihood scheme to predict the evaluation of successive actions. Based on an approximating evaluation model, the proper output (action) is always selected. The structure of the proposed FCMAC consists of three parts: a fuzzy quantizer, which is used to represent the associative mapping function from the receptive field to the actual memory; an action evaluation module, which models and produces the expected evaluation signal and an action selection unit that generates an action with the expectation of better performance using a probability distribution function that estimates an optimal action selection policy. To demonstrate its excellent performance, the proposed self-improving model is implemented as a neural network controller for the swing control of a pendulum system. The results from both the simulation and experiment demonstrates better performance and applicability of the proposed learning model.