Real-Time Collaborative Filtering Using Extreme Learning Machine

2009 ◽  
Author(s):  
Wanyu Deng ◽  
Qinghua Zheng ◽  
Lin Chen
Keyword(s):  
Collaborative Filtering ◽  
Real Time ◽  
Extreme Learning Machine ◽  
Learning Machine

scholarly journals Erratum to: Real-time transient stability status prediction using cost-sensitive extreme learning machine

2015 ◽  
Vol 27 (2) ◽  
pp. 333-333
Author(s):  
Zhen Chen ◽  
Xianyong Xiao ◽  
Changsong Li ◽  
Yin Zhang ◽  
Qingquan Hu
Keyword(s):  
Real Time ◽  
Extreme Learning Machine ◽  
Transient Stability ◽  
Learning Machine

A Real-Time Visual Tracking Approach Using Sparse Autoencoder and Extreme Learning Machine

2015 ◽  
Vol 03 (04) ◽  
pp. 267-275
Author(s):  
Liang Dai ◽  
Yuesheng Zhu ◽  
Guibo Luo ◽  
Chao He ◽  
Hanchi Lin
Keyword(s):  
Real Time ◽  
Extreme Learning Machine ◽  
Visual Tracking ◽  
Computational Cost ◽  
Real Time Tracking ◽  
Sparse Autoencoder ◽  
Selection For ◽  
Learning Machine ◽  
Hidden Layer ◽  
Online Process

Visual tracking algorithm based on deep learning is one of the state-of-the-art tracking approaches. However, its computational cost is high. To reduce the computational burden, in this paper, A real-time tracking approach is proposed by using three modules: a single hidden layer neural network based on sparse autoencoder, a feature selection for simplifying the network and an online process based on extreme learning machine. Our experimental results have demonstrated that the proposed algorithm has good performance of robust and real-time.

Real-time fault diagnosis for gas turbine generator systems using extreme learning machine

2014 ◽  
Vol 128 ◽  
pp. 249-257 ◽  
Author(s):  
Pak Kin Wong ◽  
Zhixin Yang ◽  
Chi Man Vong ◽  
Jianhua Zhong
Keyword(s):  
Fault Diagnosis ◽  
Real Time ◽  
Gas Turbine ◽  
Extreme Learning Machine ◽  
Turbine Generator ◽  
Learning Machine

scholarly journals A Real-Time BOD Estimation Method in Wastewater Treatment Process Based on an Optimized Extreme Learning Machine

2019 ◽  
Author(s):  
Ping Yu ◽  
Jie Cao ◽  
Veeriah Jegatheesan ◽  
Xianjun Du
Keyword(s):  
Wastewater Treatment ◽  
Real Time ◽  
Extreme Learning Machine ◽  
Search Algorithm ◽  
Measurement Data ◽  
Estimation Method ◽  
Cuckoo Search ◽  
Bp Network ◽  
Learning Machine ◽  
Hidden Layer

It is difficult to capture the real-time online measurement data for biochemical oxygen demand (BOD) in wastewater treatment processes. An optimized extreme learning machine (ELM) based on an improved cuckoo search algorithm (ICS) is proposed in this paper for the design of soft BOD measurement model. In ICS-ELM, the input weights matrices of the extreme learning machine (ELM) and the threshold of the hidden layer are encoded as the cuckoo's nest locations. The best input weights matrices and threshold are obtained by using the strong global search ability of improved cuckoo search (ICS) algorithm. The optimal results can be used to improve the precision of forecasting based on less number of neurons of the hidden layer in ELM. Simulation results show that the soft sensor model has good real-time performance, high prediction accuracy and stronger generalization performance for BOD measurement of the effluent quality compared to other modeling methods such as back propagation (BP) network in most cases.

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