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| Parallel Extreme Learning Machine Based on Improved Particle Swarm Optimization |
| LI Wanhua1,2,3, CHEN Yuzhong1,2,3, GUO Kun1,2,3, GUO Songrong1,2,3, LIU Zhanghui1,2 |
| 1.College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350116.2.Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing, .Fuzhou University, Fuzhou 350116.3.Fujian Collaborative Innovation Center for Big Data Applications in Governments, Fuzhou 350003 |
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Abstract To improve the stability of extreme learning machine(ELM), an extreme learning machine based on improved particle swarm optimization (IPSO-ELM) is proposed. By combining the improved particle swarm optimization with ELM, IPSO-ELM can find the optimal number of nodes in the hidden layer as well as the optimal input weights and hidden biases. Furthermore, a mutation operator is introduced into IPSO-ELM to enhance the diversity of swarm and improve the convergence speed of the random search process. Then, to handle the large-scale electrical load data, a parallel version of IPSO-ELM named PIPSO-ELM is implemented with the popular parallel computing framework Spark. Experimental results of real-life electrical load data show that PIPSO-ELM obtains better stability and scalability with higher efficiency in large-scale electrical load prediction.
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Received: 11 January 2016
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| About author:: LI Wanhua, born in 1991, master student. Her research interests include cloud computing and data mining.CHEN Yuzhong(Corresponding author), born in 1979, Ph.D., associate professor. His research interests include computational intelligence, complex networks and data mining.GUO Kun, born in 1979, Ph.D., associate professor. His research interests including complex networks and data mining.GUO Songrong, born in 1991, master student. His research interests include cloud computing and data mining.LIU Zhanghui, born in 1971, master, associate professor. His research interests include data mining.) |
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2016, Vol. 29 
