Regular Issue Papers Reinforcement Structureparameter Learning for Neural-Network-Based Fuzzy Logic Control Systems

Abstruct- This paper proposes a reinforcement neural-network-based fuzzy logic control system (RNN-FLCS) for solving various reinforcement learning problems. The proposed RNN-FLCS is constructed by integrating two neural-network-based fuzzy logic controllers (NN-FLC’s), each of which is a connectionist model with a feedforward multilayered network developed for the realization of a fuzzy logic controller. One NN-FLC performs as a fuzzy predictor, and the other as a fuzzy controller. Using the temporal difference prediction method, the fuzzy predictor can predict the external reinforcement signal and provide a more informative internal reinforcement signal to the fuzzy controller. The fuzzy controller performs a stochastic exploratory algorithm to adapt itself according to the internal reinforcement signal. During the learning process, both structure learning and parameter learning are performed simultaneously in the two NN-FLC’s using the fuzzy similarity measure. The proposed RNN-FLCS can construct a fuzzy logic control and decision-making system automatically and dynamically through a reward/penalty signal (i.e., a “good ” or “bad ” signal) or through very simple fuzzy information feedback such as “high,” “too high, ” “low, ” and “too low. ” The proposed RNN-FLCS is best applied to the learning environment, where obtaining exact training data is expensive. The proposed RNN-FLCS also preserves the advantages of the original NN-FLC, such as the ability to find proper network structure and parameters simultaneously and dynamically and to avoid the rule-matching time of the inference engine in the traditional fuzzy logic systems. Computer simulations were conducted to illustrate the performance and applicability of the proposed RNN-FLCS. I