Genetic Reinforcement Learning for Neurocontrol Problems
Machine Learning - Special issue on genetic algorithms
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
Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering
Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering
Neuro-fuzzy adaptive control based on dynamic inversion for robotic manipulators
Fuzzy Sets and Systems - Special issue: Fuzzy set techniques for intelligent robotic systems
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
A Cooperative approach to particle swarm optimization
IEEE Transactions on Evolutionary Computation
Identification of nonlinear dynamic systems using functional linkartificial neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
GA-based fuzzy reinforcement learning for control of a magneticbearing system
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
A hybrid of genetic algorithm and particle swarm optimization for recurrent network design
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
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An online self-constructing neural fuzzy inference network and its applications
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Self-organizing neuro-fuzzy system for control of unknown plants
IEEE Transactions on Fuzzy Systems
Combination of online clustering and Q-value based GA for reinforcement fuzzy system design
IEEE Transactions on Fuzzy Systems
A Functional-Link-Based Neurofuzzy Network for Nonlinear System Control
IEEE Transactions on Fuzzy Systems
A hybrid evolutionary learning algorithm for TSK-type fuzzy model design
Mathematical and Computer Modelling: An International Journal
State observer design for nonlinear systems using neural network
Applied Soft Computing
Artificial Neural Network trained by Particle Swarm Optimization for non-linear channel equalization
Expert Systems with Applications: An International Journal
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This study presents a reinforcement evolutionary learning algorithm (REL) for the self-evolving neural fuzzy inference networks (SENFIN). By applying functional link neural networks (FLNN) as the consequent part of the fuzzy rules, the proposed SENFIN model combines orthogonal polynomials and linearly independent functions in a functional expansion of the FLNN. The SENFIN model can generate the consequent part of a nonlinear combination of the input variables. An efficient reinforcement evolutionary learning algorithm (REL), which consists of structure learning and parameter learning, is also presented. The structure learning is to determine the number of fuzzy rules. It adopts a subgroup symbiotic evolution to yield several variable fuzzy systems and uses an elite-based structure strategy to find the suitable number of fuzzy rules for solving a specific problem. The parameter learning is to adjust parameters of the SENFIN. It is a hybrid evolutionary algorithm, i.e., combining the cooperative particle swarm optimization and the cultural algorithm, called the cultural cooperative particle swarm optimization (CCPSO). As the result, the CCPSO approach can increase the global search capacity by using the belief space. In this paper the proposed NFIN with an efficient reinforcement evolutionary learning algorithm had been evaluated by two reinforcement learning applications, i.e., to balance the cart-pole system and the ball and beam system. Experimental results have demonstrated that the proposed approach performs well in reinforcement learning problems.