1992/01/01 by H.R. Berenji, P. Khedkar, P.S. Khedkar · 6 citations
Computer Science · #Evolutionary Algorithms and Applications #Fuzzy Logic and Control Systems #Neural Networks and Applications
paper · doi:10.1109/72.159061
openalex publication_date 1992/01/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/02
A method for learning and tuning a fuzzy logic controller based on reinforcements from a dynamic system is presented. It is shown that: the generalized approximate-reasoning-based intelligent control (GARIC) architecture learns and tunes a fuzzy logic controller even when only weak reinforcement, such as a binary failure signal, is available; introduces a new conjunction operator in computing the rule strengths of fuzzy control rules; introduces a new localized mean of maximum (LMOM) method in combining the conclusions of several firing control rules; and learns to produce real-valued control actions. Learning is achieved by integrating fuzzy inference into a feedforward network, which can then adaptively improve performance by using gradient descent methods. The GARIC architecture is applied to a cart-pole balancing system and demonstrates significant improvements in terms of the speed of learning and robustness to changes in the dynamic system's parameters over previous schemes for cart-pole balancing.