Efficient reinforcement learning through symbiotic evolution
Machine Learning - Special issue on reinforcement learning
Evolving fuzzy rule based controllers using genetic algorithms
Fuzzy Sets and Systems
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Discovering Knowledge in Data: An Introduction to Data Mining
Discovering Knowledge in Data: An Introduction to Data Mining
A Data Mining Based Genetic Algorithm
SEUS-WCCIA '06 Proceedings of the The Fourth IEEE Workshop on Software Technologies for Future Embedded and Ubiquitous Systems, and the Second International Workshop on Collaborative Computing, Integration, and Assurance (SEUS-WCCIA'06)
Searching for diverse, cooperative populations with genetic algorithms
Evolutionary Computation
Genetic reinforcement learning through symbiotic evolution forfuzzy controller design
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
VGA-Classifier: design and applications
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Prediction and identification using wavelet-based recurrent fuzzy neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Fuzzy wavelet networks for function learning
IEEE Transactions on Fuzzy Systems
Reinforcement Hybrid Evolutionary Learning for Recurrent Wavelet-Based Neurofuzzy Systems
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Identification and control of dynamical systems using neural networks
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
In this paper, a recurrent wavelet-based neuro-fuzzy identifier RWNFI with a self-organization hybrid evolution learning algorithm SOHELA is proposed for solving various identification problems. In the proposed SOHELA, the group-based symbiotic evolution GSE is adopted such that each group in the GSE represents a collection of only one fuzzy rule. The proposed SOHELA consists of structure learning and parameter learning. In structure learning, the proposed SOHELA uses the self-organization algorithm SOA to determine a suitable rule number in the RWNFI. In parameter learning, the proposed SOHELA uses the data mining-based selection method DMSM and the data mining-based crossover method DMCM to determine groups and parent groups using the data mining method called the frequent pattern growth FP-Growth method. Based on identification simulations, the excellent performance of the proposed SOHELA compares with other various existing models.