Efficient reinforcement learning through symbiotic evolution
Machine Learning - Special issue on reinforcement learning
Evolutionary Optimization Versus Particle Swarm Optimization: Philosophy and Performance Differences
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
An analysis of cooperative coevolutionary algorithms
An analysis of cooperative coevolutionary algorithms
Integrating fuzzy knowledge by genetic algorithms
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Model-based recurrent neural network for modeling nonlinear dynamicsystems
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
Identification and control of dynamic systems using recurrent fuzzy neural networks
IEEE Transactions on Fuzzy Systems
Fuzzy tracking control design for nonlinear dynamic systems via T-S fuzzy model
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
A recurrent self-organizing neural fuzzy inference network
IEEE Transactions on Neural Networks
A note on the learning automata based algorithms for adaptive parameter selection in PSO
Applied Soft Computing
A multi-population cooperative particle swarm optimizer for neural network training
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
A novel particle swarm optimizer using optimal foraging theory
ICIC'06 Proceedings of the 2006 international conference on Computational Intelligence and Bioinformatics - Volume Part III
Restoration of epipolar line based on multi-population cooperative particle swarm optimization
ICIC'11 Proceedings of the 7th international conference on Intelligent Computing: bio-inspired computing and applications
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A new fuzzy modeling method using Multi-population Cooperative Particle Swarm Optimizer (MCPSO) for identification and control of nonlinear dynamic systems is presented in this paper. In MCPSO, the population consists of one master swarm and several slave swarms. The slave swarms executeParticle Swarm Optimization (PSO) or its variants independently to maintain the diversity of particles, while the particles in the master swarm enhance themselves based on their own knowledge and also the knowledge of the particles in the slave swarms. The MCPSO is used to automatic design of fuzzy identifier and fuzzy controller for nonlinear dynamic systems. The proposed algorithm (MCPSO) is shown to outperform PSO and some other methods in identifying and controlling dynamic systems.