Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Swarm intelligence
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
The particle swarm optimization algorithm: convergence analysis and parameter selection
Information Processing Letters
An analysis of particle swarm optimizers
An analysis of particle swarm optimizers
Particle Swarm Optimization of Neural Network Architectures andWeights
HIS '07 Proceedings of the 7th International Conference on Hybrid Intelligent Systems
Using Reservoir Computing for Forecasting Time Series: Brazilian Case Study
HIS '08 Proceedings of the 2008 8th International Conference on Hybrid Intelligent Systems
Efficient population utilization strategy for particle swarm optimizer
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Simplifying Particle Swarm Optimization
Applied Soft Computing
Genetic algorithm for reservoir computing optimization
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Adaptive particle swarm optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
The introduction of time-scales in reservoir computing, applied to isolated digits recognition
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Event detection and localization in mobile robot navigation using reservoir computing
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
An Optimization Methodology for Neural Network Weights and Architectures
IEEE Transactions on Neural Networks
Hybrid Training Method for MLP: Optimization of Architecture and Training
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An automatic method for construction of ensembles to time series prediction
International Journal of Hybrid Intelligent Systems
| Hi-index | 0.00 |
Reservoir Computing is a paradigm of artificial neural networks that has obtained promising results. However there are some disadvantages: the reservoir is created randomly and needs to be large enough to be able to capture all the features of the data. For this work we use PSO --- Particle Swarm Optimization to optimize the initial parameters of the Reservoir Computing. The results obtained with the optimization method are compared with results obtained by an exhaustive search for global parameters generation of Reservoir Computing. Five time series were used to show that the optimization method reduces the number of training cycles required to train the system.