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• W3136996429 abstract "Quaternion random neural network trained by extreme learning machine (Q-ELM) becomes attractive for its good learning capability and generalization performance in 3 or 4-dimensional (3/4-D) hypercomplex data learning. But how to determine the optimal network architecture is always challenging in Q-ELM. To this end, a novel error-minimization-based Q-ELM (QEM-ELM) that only needs to optimize the output weights of the newly added neuron is developed in this brief. On this basis, a dynamic network construction scheme is further extended on Q-ELM, leading to a novel DQ-ELM, where the hidden nodes can be dynamically recruited or deleted according to the significance to network performance. The network parameters can be optimized and the architecture can be self-adapted simultaneously. Simulation results on many benchmark datasets demonstrate that the proposed QEM-ELM and DQ-ELM achieve good generalization performance by preserving a compact network size." @default.
• W3136996429 created "2021-03-29" @default.
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• W3136996429 date "2021-08-01" @default.
• W3136996429 modified "2023-10-08" @default.
• W3136996429 title "Dynamic Quaternion Extreme Learning Machine" @default.
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• W3136996429 doi "https://doi.org/10.1109/tcsii.2021.3067014" @default.
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