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Hierarchical Bi-level Multi-Objective Evolution of Single- and Multi-layer Echo State Network Autoencoders for Data Representations.

, , and . CoRR, (2018)

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PSO-based analysis of Echo State Network parameters for time series forecasting., , , and . Appl. Soft Comput., (2017)Genesis of Basic and Multi-Layer Echo State Network Recurrent Autoencoders for Efficient Data Representations., , and . CoRR, (2018)Adaptive Extreme Learning Machine for Recurrent Beta-basis Function Neural Network Training, and . (2018)cite arxiv:1810.13135Comment: 14 pages and 5 figures.BrainOS: A Novel Artificial Brain-Alike Automatic Machine Learning Framework., , , , , and . Frontiers Comput. Neurosci., (2020)Bi-level multi-objective evolution of a Multi-Layered Echo-State Network Autoencoder for data representations., , , and . Neurocomputing, (2019)Novel single and multi-layer echo-state recurrent autoencoders for representation learning., , , and . Eng. Appl. Artif. Intell., (2022)A Hybrid Approach Based on Particle Swarm Optimization for Echo State Network Initialization., , , , and . SMC, page 2896-2901. IEEE, (2015)Hierarchical Bi-level Multi-Objective Evolution of Single- and Multi-layer Echo State Network Autoencoders for Data Representations., , and . CoRR, (2018)Single- and multi-objective particle swarm optimization of reservoir structure in Echo State Network., , , , and . IJCNN, page 440-447. IEEE, (2016)Learning to Walk Using a Recurrent Neural Network with Time Delay., , , , , and . ICANN, volume 8131 of Lecture Notes in Computer Science, page 511-518. Springer, (2013)