Reinforcement Learning-Based Differential Evolution With Cooperative Coevolution for a Compensatory Neuro-Fuzzy Controller

This paper presents the integration of reinforcement learning-based differential evolution (DE) with the cooperative coevolution (R-CCDE) method in a compensatory neuro-fuzzy controller (CNFC). The CNFC model employs compensatory fuzzy operations, which increase the adaptability and effectiveness of...

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Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems 2018-10, Vol.29 (10), p.4719-4729
Main Authors: Chen, Cheng-Hung, Liu, Chong-Bin
Format: Article
Language:English
Subjects:
Adaptability
Artificial neural networks
Biological neural networks
Coevolution
Computer simulation
Cooperative coevolution
differential evolution (DE)
Encoding
Evolution
Fitness
Fuzzy control
Fuzzy logic
Input variables
Learning (artificial intelligence)
Machine learning
neuro-fuzzy controller (NFC)
Nonlinear control
nonlinear system problems
Nonlinear systems
Reinforcement
reinforcement learning
Reproductive fitness
Sociology
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Summary:This paper presents the integration of reinforcement learning-based differential evolution (DE) with the cooperative coevolution (R-CCDE) method in a compensatory neuro-fuzzy controller (CNFC). The CNFC model employs compensatory fuzzy operations, which increase the adaptability and effectiveness of the controller. The R-CCDE method was used to determine an adequate control policy for nonlinear system problems. The evolution of a population involved the use of DE with cooperative coevolution to adjust CNFC parameters, and the fitness function of the R-CCDE method is used by a reinforcement signal to determine the controller that can be used to solve the control problem. This paper identified the best performing controller to solve nonlinear system problems. The simulation results of the proposed R-CCDE method were compared with those of various DE methods and the performance of the proposed R-CCDE method was superior to that of the other methods.
ISSN:2162-237X
2162-2388
DOI:10.1109/TNNLS.2017.2772870