Designing a hierarchical fuzzy logic controller using the differential evolution approach
Applied Soft Computing
A fuzzy Actor-Critic reinforcement learning network
Information Sciences: an International Journal
An adaptive neuro-fuzzy system for efficient implementations
Information Sciences: an International Journal
NEFCLASS based extraction of fuzzy rules and classification of risks of low back disorders
Expert Systems with Applications: An International Journal
An adaptive neuro-fuzzy model for prediction of student's academic performance
Computers and Industrial Engineering
Adaptive controller with fuzzy rules emulated structure and its applications
Engineering Applications of Artificial Intelligence
Expert Systems with Applications: An International Journal
A recursive rule base adjustment algorithm for a fuzzy logic controller
Fuzzy Sets and Systems
An application of neuro-fuzzy technology for analysis of the CO2 capture process
Fuzzy Sets and Systems
A Fuzzy Logic Controller tuned with PSO for 2 DOF robot trajectory control
Expert Systems with Applications: An International Journal
A recurrent neuro-fuzzy system and its application in inferential sensing
Applied Soft Computing
Modeling of the carbon dioxide capture process system using machine intelligence approaches
Engineering Applications of Artificial Intelligence
Computers and Industrial Engineering
Reinforcement learning approach to goal-regulation in a self-evolutionary manufacturing system
Expert Systems with Applications: An International Journal
Artificial Intelligence in Medicine
Hierarchical neuro-fuzzy call admission controller for ATM networks
Computer Communications
Generation of a probabilistic fuzzy rule base by learning from examples
Information Sciences: an International Journal
eT2FIS: An Evolving Type-2 Neural Fuzzy Inference System
Information Sciences: an International Journal
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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