Introduction to the theory of neural computation
Introduction to the theory of neural computation
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Closed-Loop Object Recognition Using Reinforcement Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dynamic Programming and Optimal Control
Dynamic Programming and Optimal Control
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Neuro-Dynamic Programming
Learning cost-sensitive active classifiers
Artificial Intelligence
Learning to Predict by the Methods of Temporal Differences
Machine Learning
Temporal credit assignment in reinforcement learning
Temporal credit assignment in reinforcement learning
Delayed reinforcement learning for adaptive image segmentation andfeature extraction
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
An ART-based fuzzy adaptive learning control network
IEEE Transactions on Fuzzy Systems
Reinforcement structure/parameter learning for neural-network-based fuzzy logic control systems
IEEE Transactions on Fuzzy Systems
Reinforcement learning for an ART-based fuzzy adaptive learning control network
IEEE Transactions on Neural Networks
HebbR2-Taffic: A novel application of neuro-fuzzy network for visual based traffic monitoring system
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Stock trading with cycles: A financial application of ANFIS and reinforcement learning
Expert Systems with Applications: An International Journal
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Reinforcement learning has been widely-used for applications in planning, control, and decision making. Rather than using instructive feedback as in supervised learning, reinforcement learning makes use of evaluative feedback to guide the learning process. In this paper, we formulate a pattern classification problem as a reinforcement learning problem. The problem is realized with a temporal difference method in a FALCON-R network. FALCON-R is constructed by integrating two basic FALCON-ART networks as function approximators, where one acts as a critic network (fuzzy predictor) and the other as an action network (fuzzy controller). This paper serves as a guideline in formulating a classification problem as a reinforcement learning problem using FALCON-R. The strengths of applying the reinforcement learning method to the pattern classification application are demonstrated. We show that such a system can converge faster, is able to escape from local minima, and has excellent disturbance rejection capability.