A neural fuzzy network for word information processing
Fuzzy Sets and Systems - Special issue: Approximate Reasoning in Words
Evolution of fuzzy behaviors for multi-robotic system
Robotics and Autonomous Systems
A fuzzy Actor-Critic reinforcement learning network
Information Sciences: an International Journal
Computers & Mathematics with Applications
Intelligent sensory evaluation: Concepts, implementations, and applications
Mathematics and Computers in Simulation
Pattern recognition of guided waves for damage evaluation in bars
Pattern Recognition Letters
Use of neurofuzzy networks to improve wastewater flow-rate forecasting
Environmental Modelling & Software
Expert Systems with Applications: An International Journal
RFCMAC: A novel reduced localized neuro-fuzzy system approach to knowledge extraction
Expert Systems with Applications: An International Journal
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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 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. It 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. Computer simulations were conducted to illustrate its performance and applicability