Time series prediction with improved neuro-endocrine model.
The paper is focused on improving the performance of neuro-endocrine models with considering the interaction of glands. Comparing to conventional neuro-endocrine models, the concentration of hormone of one gland is modulated by those of others, and the weights of cells are modulated by the improved endocrine system. The interacted equation among all glands is designed and the parameters of them are chosen with theory analysis. Because all the parameters of the model are constants when the system reaches the equilibrium state, particle swarm optimization algorithm is utilized to search the optimal parameters of the model. The theory analysis indicates that the performance of neuro-endocrine model is better than or at least equal to that of corresponding artificial neural network. To indicate the effectiveness of the proposed model, some time series from different research fields, which are used in some literatures, are tested with the proposed model, the results indicate that the proposed model has some good performance.
The School of Physics and Electronic Information, Huai Bei Normal University, Huaibei, 235000 China.
Debao
D
Chen
The School of Physics and Electronic Information, Huai Bei Normal University, Huaibei, 235000 China.
Jiangtao
J
Wang
The School of Physics and Electronic Information, Huai Bei Normal University, Huaibei, 235000 China.
Feng
F
Zou
The School of Physics and Electronic Information, Huai Bei Normal University, Huaibei, 235000 China.
Wujie
W
Yuan
The School of Mathematical Science, Huai Bei Normal University, Huaibei, 235000 China.
Weibo
W
Hou
Neural network; Neuro-endocrine model; Particle swarm optimization (PSO); Time series prediction