Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Using Reservoir Computing for Forecasting Time Series: Brazilian Case Study
HIS '08 Proceedings of the 2008 8th International Conference on Hybrid Intelligent Systems
On the Quantification of Dynamics in Reservoir Computing
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
Genetic algorithm for reservoir computing optimization
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
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
Survey: Reservoir computing approaches to recurrent neural network training
Computer Science Review
Expert Systems with Applications: An International Journal
An automatic method for construction of ensembles to time series prediction
International Journal of Hybrid Intelligent Systems
Hi-index | 12.05 |
Reservoir computing is a framework for computation like a recurrent neural network that allows for the black box modeling of dynamical systems. In contrast to other recurrent neural network approaches, reservoir computing does not train the input and internal weights of the network, only the readout is trained. However it is necessary to adjust parameters to create a ''good'' reservoir for a given application. In this study we introduce a method, called RCDESIGN (reservoir computing and design training). RCDESIGN combines an evolutionary algorithm with reservoir computing and simultaneously looks for the best values of parameters, topology and weight matrices without rescaling the reservoir matrix by the spectral radius. The idea of adjust the spectral radius within the unit circle in the complex plane comes from the linear system theory. However, this argument does not necessarily apply to nonlinear systems, which is the case of reservoir computing. The results obtained with the proposed method are compared with results obtained by a genetic algorithm search for global parameters generation of reservoir computing. Four time series were used to validate RCDESIGN.