Neural-Network-Based Fuzzy Logic Control and Decision System
IEEE Transactions on Computers - Special issue on artificial neural networks
Applications of Automatic Control Concepts to Traffic Flow Modeling and Control
Applications of Automatic Control Concepts to Traffic Flow Modeling and Control
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Neurodynamics of Cognition and Consciousness
Neurodynamics of Cognition and Consciousness
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Freeway ramp metering: an overview
IEEE Transactions on Intelligent Transportation Systems
IEEE Transactions on Neural Networks
Online learning control by association and reinforcement
IEEE Transactions on Neural Networks
Neural Networks for Improved Tracking
IEEE Transactions on Neural Networks
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This paper aims to efficiently deal with the problems of multiple ramps metering. A new method which is called neuro-fuzzy adaptive dynamic programming with eligibility traces (NFADP(λ)) is proposed. With the introduction of neuro-fuzzy and eligibility traces, the performance of ADP is greatly enhanced. First of all, the expert experience is introduced to ADP, therefore the convergence of ADP is greatly reinforced. Second, with the learning strategy revised, the training of action network is accelerated. In order to achieve multiple ramps metering control, special performance index function is established in NFADP(λ). Extensive simulation on a hypothetical freeway are carried out with NFADP(λ), compared to ALINEA as a stand-alone strategy. Simulation results indicate that NFADP(λ) have good performances in both alleviating stochastic variations of the traffic demand and congestion situations.