An extreme learning process neural networks based on particle swarm optimization

LIU Zhi-gang; XU Shao-hua; LI Pan-chi

doi:10.3969/j.issn.1000-5641.2016.04.010

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LIU Zhi-gang, XU Shao-hua, LI Pan-chi. An extreme learning process neural networks based on particle swarm optimization[J]. Journal of East China Normal University (Natural Sciences), 2016, (4): 86-95. doi: 10.3969/j.issn.1000-5641.2016.04.010

Citation:

LIU Zhi-gang, XU Shao-hua, LI Pan-chi. An extreme learning process neural networks based on particle swarm optimization[J]. Journal of East China Normal University (Natural Sciences), 2016, (4): 86-95. doi: 10.3969/j.issn.1000-5641.2016.04.010

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An extreme learning process neural networks based on particle swarm optimization

doi: 10.3969/j.issn.1000-5641.2016.04.010

1.
School of Computer and Information Technology, Northeast Petroleum University, Daqing Heilongjiang 163318, China;
2.
College of Information Science and Engineering, Shandong University of Science and Technology, Qingdao Shandong 266590, China

Received Date: 2015-06-12
Publish Date: 2016-07-25

Abstract

Abstract

Aiming at the problems that process neural network has more learning parameters, sensitive to initial value, complicated computation and difficult to converge for the gradient descent algorithm based on orthogonal basis expansion, a new process neural network based on extreme learning machine is presented in this paper. The iterative adjustment strategy is rejected in the trainning process and use Moore-Penrose to calculate the output weight matrix. In order to make up for the lack of random assignment for the extreme learning machine, the particle swarm algorthim is taken and the parameters are optimized with its global search ability. This algorthim can get the more tightly network structure and improve the model generalization ability. The model and algorthim are applied to Henon chaotic time series and sunspot prediction. The simulation resultsconfirm the validity and feasibility of the model and learning algorithm.
- process neural network extreme learning machine particle swarm Moore-Penrose generalized inverse network training,

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References(1)

References

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