Efficient reinforcement learning through symbiotic evolution
Machine Learning - Special issue on reinforcement learning
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Adaptive Control
An Efficient Fuzzy C-Means Clustering Algorithm
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Searching for diverse, cooperative populations with genetic algorithms
Evolutionary Computation
An asymmetry-similarity-measure-based neural fuzzy inference system
Fuzzy Sets and Systems
Tuning of a neuro-fuzzy controller by genetic algorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Genetic reinforcement learning through symbiotic evolution forfuzzy controller design
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A novel genetic algorithm based on immunity
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
The possibilistic C-means algorithm: insights and recommendations
IEEE Transactions on Fuzzy Systems
An ART-based fuzzy adaptive learning control network
IEEE Transactions on Fuzzy Systems
Implementation of evolutionary fuzzy systems
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Selecting fuzzy if-then rules for classification problems using genetic algorithms
IEEE Transactions on Fuzzy Systems
GenSoFNN: a generic self-organizing fuzzy neural network
IEEE Transactions on Neural Networks
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This paper presents an efficient immune symbiotic evolution learning (ISEL) algorithm for the compensatory neurofuzzy controller (CNFC). The proposed ISEL method includes three major components--initial population, subgroup symbiotic evolution, and immune system algorithm. First, the self-clustering algorithm that determines proper input space partitioning and finds the mean and variance of the Gaussian membership functions and number of rules is applied to the initial population. Second, the subgroup symbiotic evolution method that uses each subantibody represents a single fuzzy rule and the evolution of the rule itself. Third, the immune system algorithm uses the clonal selection principle, such that antibodies between others of high similar degree are canceled, and these antibodies, after processing, will have higher quality, accelerating the search, and increasing the global search capacity. Finally, the proposed CNFC with ISEL (CNFC-ISEL) method is adopted to solve several nonlinear control problems. The simulation results have shown that the proposed CNFC-ISEL can outperform other methods.