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Adaptive Extreme Learning Machine for Recurrent Beta-basis Function Neural Network Training
In this paper, new architecture and training algorithm are proposed for the BBFNN.
Hierarchical Bi-level Multi-Objective Evolution of Single- and Multi-layer Echo State Network Autoencoders for Data Representations
Multi-objective Particle Swarm Optimization (MOPSO) is used to optimize ESN structure in a way to provide a trade-off between the network complexity decreasing and the accuracy increasing.
Genesis of Basic and Multi-Layer Echo State Network Recurrent Autoencoders for Efficient Data Representations
In this paper, the noticeable dynamism and the large memory provided by ESN and the strength of Autoencoders in feature extraction are gathered within an ESN Recurrent Autoencoder (ESN-RAE).
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