J
Jiuwen Cao
Researcher at Hangzhou Dianzi University
Publications - 185
Citations - 4340
Jiuwen Cao is an academic researcher from Hangzhou Dianzi University. The author has contributed to research in topics: Extreme learning machine & Computer science. The author has an hindex of 29, co-authored 151 publications receiving 3029 citations. Previous affiliations of Jiuwen Cao include University of Electronic Science and Technology of China & Nanyang Technological University.
Papers
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Proceedings ArticleDOI
Epileptic State Classification based on Intrinsic Mode Function and Wavelet Packet Decomposition
TL;DR: This paper studies the epileptic preictal state classification for seizure prediction using the benchmark epilepsy EEG database collected by the Children’s Hospital Boston and the Massachusetts Institute of Technology using several popular classifiers.
Journal ArticleDOI
Dynamic Quaternion Extreme Learning Machine
TL;DR: 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, leading to a novel DQ-ELm, where the hidden nodes can be dynamically recruited or deleted according to the significance to network performance.
Journal ArticleDOI
Regional Scalp EEGs Analysis and Classification on Typical Childhood Epilepsy Syndromes
TL;DR: Wang et al. as discussed by the authors presented a comprehensive analysis on the correlation between time/frequency-domain regional scalp EEG features and typical epilepsy syndromes, and proposed a transfer network-based classification model for epilepsy disorders.
Journal ArticleDOI
Estimation of Continuous Joint Angles of Upper Limb Based on sEMG by Using GA-Elman Neural Network
TL;DR: The wavelet packet energy entropy (WPEE) of the special subspace was investigated as a feature of the sEMG signal and an Elman neural network optimized by genetic algorithm was established to estimate the joint angle of shoulder and elbow.
Journal ArticleDOI
Proportionate adaptive filtering algorithms based on mixed square/fourth error criterion with unbiasedness criterion for sparse system identification
TL;DR: Simulation results demonstrate that the proposed PLMS/F and bias‐compensated PL MS/F algorithms can achieve excellent identification performance in terms of steady‐state misalignment and convergence speed under noisy input and non‐Gaussian output noise environments.