scholarly journals Parameter Optimization of Kernel Extreme Learning Machine Using Artificial Bee Colony Algorithm and Its Application for Disease Classification

2020 ◽  
Vol 10 (3) ◽  
pp. 127-136
Author(s):  
Ming-Huwi Horng ◽  
◽  
Jian-Ying Cheng ◽  
Yu-Lun Hung ◽  
Yu-Cheng Hung ◽  
...  
Keyword(s):  
Extreme Learning Machine ◽  
Parameter Optimization ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Disease Classification ◽  
Bee Colony ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine

Artificial bee colony algorithm–optimized error minimized extreme learning machine and its application in short-term wind speed prediction

2018 ◽  
Vol 43 (3) ◽  
pp. 263-276 ◽  
Author(s):  
Zhongda Tian ◽  
Gang Wang ◽  
Shujiang Li ◽  
Yanhong Wang ◽  
Xiangdong Wang
Keyword(s):  
Wind Speed ◽  
Prediction Model ◽  
Extreme Learning Machine ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Short Term ◽  
Machine Model ◽  
Bee Colony ◽  
Learning Machine ◽  
Hidden Layer

In order to improve the prediction accuracy of short-term wind speed, a short-term wind speed prediction model based on artificial bee colony algorithm optimized error minimized extreme learning machine model is proposed. The extreme learning machine has the advantages of fast learning speed and strong generalization ability. But many useless neurons of incremental extreme learning machine have little influences on the final output and, at the same time, reduce the efficiency of the algorithm. The optimal parameters of the hidden layer nodes will make network output error of incremental extreme learning machine decrease with fast speed. Based on the error minimized extreme learning machine, artificial bee colony algorithm is introduced to optimize the parameters of the hidden layer nodes, decrease the number of useless neurons, reduce training and prediction error, achieve the goal of reducing the network complexity, and improve the efficiency of the algorithm. The error minimized extreme learning machine prediction model is constructed with the obtained optimal parameters. The stability and convergence property of artificial bee colony algorithm optimized error minimized extreme learning machine model are proved. The practical short-term wind speed time series is used as the research object and to verify the validity of the prediction model. Multi-step prediction simulation of short-term wind speed is carried out. Compared with other prediction models, simulation results show that the prediction model proposed in this article reduces the training time of the prediction model and decreases the number of hidden layer nodes. The prediction model has higher prediction accuracy and reliability performance, meanwhile improves the performance indicators.

A particle swarm inspired multi-elitist artificial bee colony algorithm for real-parameter optimization

2013 ◽  
Vol 57 (2) ◽  
pp. 493-516 ◽  
Author(s):  
Yi Xiang ◽  
Yuming Peng ◽  
Yubin Zhong ◽  
Zhenyu Chen ◽  
Xuwen Lu ◽  
...  
Keyword(s):  
Parameter Optimization ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Particle Swarm ◽  
Real Parameter ◽  
Bee Colony
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