An extreme learning process neural networks based on particle swarm optimization

doi:10.3969/j.issn.1000-5641.2016.04.010

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 results confirm the validity and feasibility of the model and learning algorithm.

Key words: process neural network, extreme learning machine, particle swarm, Moore-Penrose generalized inverse, network training

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 Sc, doi: 10.3969/j.issn.1000-5641.2016.04.010.

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